Professor Geoffrey McLachlan's research interests are in: data mining, statistical analysis of microarray, gene expression data, finite mixture models and medical statistics.
Professor McLachlan received his PhD from the University of Queensland in 1974 and his DSc from there in 1994. His current research projects in statistics are in the related fields of classification, cluster and discriminant analyses, image analysis, machine learning, neural networks, and pattern recognition, and in the field of statistical inference. The focus in the latter field has been on the theory and applications of finite mixture models and on estimation via the EM algorithm.
A common theme of his research in these fields has been statistical computation, with particular attention being given to the computational aspects of the statistical methodology. This computational theme extends to Professor McLachlan's more recent interests in the field of data mining.
He is also actively involved in research in the field of medical statistics and, more recently, in the statistical analysis of microarray gene expression data.
Journal Article: Functional mixtures-of-experts
Chamroukhi, Faïcel, Pham, Nhat Thien, Hoang, Van Hà and McLachlan, Geoffrey J. (2024). Functional mixtures-of-experts. Statistics and Computing, 34 (3) 98. doi: 10.1007/s11222-023-10379-0
Journal Article: Semi‐supervised Gaussian mixture modelling with a missing‐data mechanism in R
Lyu, Ziyang, Ahfock, Daniel, Thompson, Ryan and McLachlan, Geoffrey J. (2024). Semi‐supervised Gaussian mixture modelling with a missing‐data mechanism in R. Australian and New Zealand Journal of Statistics, 66 (2), 146-162. doi: 10.1111/anzs.12413
Journal Article: An overview of skew distributions in model-based clustering
Lee, Sharon X. and McLachlan, Geoffrey J. (2024). An overview of skew distributions in model-based clustering. Science Talks, 9 100298, 100298. doi: 10.1016/j.sctalk.2024.100298
A Novel Approach to Semi-Supervised Statistical Machine Learning
(2023–2026) ARC Discovery Projects
Classification methods for providing personalised and class decisions
(2018–2022) ARC Discovery Projects
ARC Training Centre for Innovation in Biomedical Imaging Technology
(2017–2024) ARC Industrial Transformation Training Centres
Detecting the unexpected in astronomical data using complexity based approaches
(2024) Doctor Philosophy
Role of Finite Mixture Models in Semi-Supervised Learning
Doctor Philosophy
(2023) Doctor Philosophy
The EM algorithm and extensions
McLachlan, Geoffrey J. and Krishnan, Thriyambakam (2008). The EM algorithm and extensions. 2nd ed. Hoboken, NJ, United States: John Wiley & Sons. doi: 10.1002/9780470191613
Analyzing Microarray Gene Expression Data
McLachlan, G. J., Do, K. and Ambroise, C (2004). Analyzing Microarray Gene Expression Data. New York: Wiley-Interscience.
Analyzing microarray gene expression data
McLachlan, Geoffrey J., Do, Kim-Anh and Ambroise, Christophe (2004). Analyzing microarray gene expression data. Hoboken, NJ, USA: John Wiley & Sons. doi: 10.1002/047172842x
McLachlan, G. J. and Peel, D. (2000). Finite Mixture Models. New York: John Wiley & Sons.
Finite mixture models: McLachlan/finite mixture models
McLachlan, Geoffrey and Peel, David (2000). Finite mixture models: McLachlan/finite mixture models. Hoboken, NJ, USA: John Wiley & Sons. doi: 10.1002/0471721182
The EM algorithm and extensions
McLachlan, Geoffrey J. and Krishnan, Thriyambakam (1997). The EM algorithm and extensions. New York, United States: Wiley.
Discriminant analysis and statistical pattern recognition
McLachlan, Geoffrey J. (1992). Discriminant analysis and statistical pattern recognition. Hoboken, NJ, USA: John Wiley & Sons. doi: 10.1002/0471725293
Discriminant analysis and statistical pattern recognition
McLachlan, Geoffrey John (1992). Discriminant analysis and statistical pattern recognition. New York , United States: Wiley.
Mixture models : inference and applications to clustering
McLachlan, Geoffrey J. and Basford, Kaye E. (1988). Mixture models : inference and applications to clustering. New York, United States: Marcel Dekker.
On mean and/or variance mixtures of normal distributions
Lee, S.X. and McLachlan, G.J. (2021). On mean and/or variance mixtures of normal distributions. Statistical learning and modeling in data analysis: methods and applications. (pp. 1-10) edited by S. Balzano, G.C. Porzio, R. Salvatore, D. Vistocco and M. Vichi. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-69944-4_13
Automated gating and dimension reduction of high-dimensional cytometry data
Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2021). Automated gating and dimension reduction of high-dimensional cytometry data. Mathematical, computational and experimental T cell immunology. (pp. 281-294) edited by Carmen Molina-París and Grant Lythe . Cham, Switzerland: Springer. doi: 10.1007/978-3-030-57204-4_16
Estimation of classification rules from partially classified data
McLachlan, Geoffrey and Ahfock, Daniel (2021). Estimation of classification rules from partially classified data. Data analysis and rationality in a complex world. (pp. 149-157) edited by Theodore Chadjipadelis, Berthold Lausen, Angelos Markos, Tae Rim Lee, Angela Montanari and Rebecca Nugent. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-60104-1_17
Comprehensive chemometrics: chemical and biochemical data analysis
McLachlan, G. J., Rathnayake, S. and Lee, S. X. (2020). Comprehensive chemometrics: chemical and biochemical data analysis. Comprehensive chemometrics: chemical and biochemical data analysis. (pp. 267-304) edited by Steven Brown, Roma Tauler and Beata Walczak. Oxford, United Kingdom: Elsevier.
Mixture of factor analyzers for the clustering and visualization of high-dimensional data
McLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2019). Mixture of factor analyzers for the clustering and visualization of high-dimensional data. Advances in latent class analysis: a festschrift in honor of C. Mitchell Dayton. (pp. 79-98) edited by Gregory R. Hancock, Jeffrey R. Harring and George B. Macready. Charlotte, NC, United States: Information Age Publishing.
Risk measures based on multivariate skew normal and skew t-mixture models
Lee, Sharon X. and McLachlan, Geoffrey J. (2018). Risk measures based on multivariate skew normal and skew t-mixture models. Asymmetric dependence in finance: diversification, correlation and portfolio management in market downturns. (pp. 152-168) edited by Jamie Alcock and Stephen Satchell. Chichester, West Sussex, United Kingdom: John Wiley & Sons. doi: 10.1002/9781119288992.ch7
McLachlan, G. J., Bean, R. W. and Ng, S. K. (2017). Clustering. Bioinformatics Vol. II: Structure, Function, and Applications. (pp. 345-362) edited by Jonathan M. Keith. New York, NY, United States: Humana Press. doi: 10.1007/978-1-4939-6613-4_19
Finite mixture models in biostatistics
Lee, Sharon X., Ng, Shu-Kay and McLachlan, Geoffrey J. (2017). Finite mixture models in biostatistics. Disease Modelling and Public Health, Part A. (pp. 75-102) edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne and C.R. Rao. Amsterdam, Netherlands: Elsevier. doi: 10.1016/bs.host.2017.08.005
Ng, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. Data science, innovative developments in data analysis and clustering. (pp. 43-57) edited by Francesco Palumbo, Angela Montanari and Maurizio Vichi. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-55723-6
Nguyen, Hien D., McLachlan, Geoffrey J. and Hill, Michelle M. (2017). Statistical evaluation of labeled comparative profiling proteomics experiments using permutation test. Proteome bioinformatics. (pp. 109-117) edited by Shivakumar Keerthikumar and Suresh Mathivanan. New York, NY United States: Humana Press. doi: 10.1007/978-1-4939-6740-7_9
Application of mixture models to large datasets
Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Application of mixture models to large datasets. Big data analytics: methods and applications. (pp. 57-74) edited by Saumyadipta Pyne, B. L. S. Prakasa Rao and S. B. Rao. New Delhi, India: Springer India. doi: 10.1007/978-81-322-3628-3_4
Mixture distributions - further developments
McLachlan, Geoffrey J. (2016). Mixture distributions - further developments. Wiley statsref: statistics reference online. (pp. 1-13) Chichester, United Kingdom: John Wiley & Sons. doi: 10.1002/9781118445112.stat00947.pub2
Mixture models for standard p-dimensional Euclidean data
McLachlan, Geoffrey J. and Rathnayake, Suren I. (2016). Mixture models for standard p-dimensional Euclidean data. Handbook of cluster analysis. (pp. 145-171) edited by Christian Hennig, Marina Meila, Fionn Murtagh and Roberto Rocci. Boca Raton, FL, United States: CRC Press. doi: 10.1201/b19706-14
Computation: Expectation-Maximization Algorithm
McLachlan, Geoffrey J. (2015). Computation: Expectation-Maximization Algorithm. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 469-474) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42007-6
McLachlan, Geoffrey J. (2015). Mixture Models in Statistics. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 624-628) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42055-6
Multivariate Analysis: Classification and Discrimination
McLachlan, Geoffrey (2015). Multivariate Analysis: Classification and Discrimination. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 116-120) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42150-1
Clustering of gene expression data via normal mixture models
McLachlan, G. J., Flack, L. K., Ng, S. K. and Wang, K. (2013). Clustering of gene expression data via normal mixture models. Statistical methods for microarray data analysis: methods and protocols. (pp. 103-119) edited by Andrei Y. Yakovlev, Lev Klebanov and Daniel Gaile. New York, NY, United States: Humana Press. doi: 10.1007/978-1-60327-337-4_7
An enduring interest in classification: supervised and unsupervised
McLachlan, G. J. (2012). An enduring interest in classification: supervised and unsupervised. Journeys to data mining: experiences from 15 renowned researchers. (pp. 147-171) edited by Mohamed Medhat Gaber. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-28047-4_12
Ng, Shu Kay, Krishnan, Thriyambakam and McLachlan, Geoffrey J. (2012). The EM algorithm. Handbook of Computational Statistics: Concepts and Methods. (pp. 139-172) edited by James E. Gentle, Wolfgang Karl Hardle and Yuichi Mori. Berlin & New York: Springer. doi: 10.1007/978-3-642-21551-3__6
Ng, Shu Kay, Krishnan, Thriyambakam and McLachlan, Geoffrey J. (2011). The EM Algorithm. Handbook of Computational Statistics. (pp. 139-172) Berlin, Germany: Springer. doi: 10.1007/978-3-642-21551-3_6
Mixtures of factor analyzers for the analysis of high-dimensional data
McLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2011). Mixtures of factor analyzers for the analysis of high-dimensional data. Mixture estimation and applications. (pp. 189-212) edited by Kerrie L. Mengersen, Christian P. Robert and D. Michael Titterington. Chichester, United Kingdom: John Wiley and Sons. doi: 10.1002/9781119995678.ch9
Clustering of high-dimensional and correlated data
McLachlan, Geoffrey J., Ng, Shu-Kay and Wang, K. (2010). Clustering of high-dimensional and correlated data. Data Analysis and Classification: Proceedings of the 6th Conference of the Classification and Data Analysis Group of the SocietàItaliana di Statistica, Macerata, Italy 12-14 September, 2007. (pp. 3-11) edited by Francesco Palumbo, Carlo Natale Lauro and Michael J. Greenacre. Berlin; Heidelberg, Germany: Springer - Verlag. doi: 10.1007/978-3-642-03739-9_1
Clustering of high-dimensional data via finite mixture models
McLachlan, Geoff J. and Baek, Jangsun (2010). Clustering of high-dimensional data via finite mixture models. Advances in Data Analysis, Business Intelligence: Proceedings of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Joint Conference with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC Helmut-Schmidt-University, Hamburg, July 16–18, 2008. (pp. 33-44) edited by Andreas Fink, Berthold Lausen, Wilfried Seidel and Alfred Ultsch. Heidelberg, Germany: Springer-Verlag. doi: 10.1007/978-3-642-01044-6
Ng, Shu-Kay and McLachlan, Geoffrey J. (2010). Expert networks with mixed continuous and categorical feature variables: A location modeling approach.. Machine learning research progress. (pp. 355-368) edited by Hannah Peters and Mia Vogel. New York, U.S.A.: Nova Science.
Use of mixture models in multiple hypothesis testing with applications in bioinformatics
McLachlan, Geoffrey J. and Wockner, Leesa (2010). Use of mixture models in multiple hypothesis testing with applications in bioinformatics. Classification as a Tool for Research: Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft für Klassifikation. (pp. 177-184) edited by Hermann Locarek-Junge and Claus Weihs. Heidelberg, Germany: Springer-Verlag. doi: 10.1007/978-3-642-10745-0
Clustering methods for gene-expression data
Flack, L. K. and McLachlan, G. J. (2009). Clustering methods for gene-expression data. Handbook of Research on Systems Biology Applications in Medicine. (pp. 209-220) edited by Andriani Daskalaki. United States: IGI Global. doi: 10.4018/978-1-60566-076-9.ch011
McLachlan, G. J. and Ng, S-K. (2009). EM. The Top Ten Algorithms in Data Mining. (pp. 93-115) edited by Wu, X. and Kumar, V.. Florida, United States: Chapman & Hall/CRC. doi: 10.1201/9781420089653-12
McLachlan, G. J. (2009). Model-based clustering. Comprehensive chemometrics: chemical and biochemical data analysis. (pp. 655-681) edited by Steven D. Brown, Roma Tauler and Beata Walczak. Oxford, U.K.: Elsevier Science. doi: 10.1016/B978-044452701-1.00068-5
Statistical analysis on microarray data: selection of gene prognosis signatures
Le Cao, Kim-Anh and McLachlan, Geoffrey J. (2009). Statistical analysis on microarray data: selection of gene prognosis signatures. Computational biology: issues and applications in oncology. (pp. 55-76) edited by Tuan Pham. New York, United States: Springer. doi: 10.1007/978-1-4419-0811-7_3
McLachlan, G. J., Bean, R. W. and Ng, S.-K. (2008). Clustering. Bioinformatics, volume 2: Structure, function and applications. (pp. 423-439) edited by J. M. Keith. New Jersey, United States: Humana Press. doi: 10.1007/978-1-60327-429-6_22
Clustering of microarray data via mixture models
McLachlan, Geoffrey J., Ng, Angus and Bean, Richard W. (2008). Clustering of microarray data via mixture models. Statistical advances in the biomedical sciences: clinical trials, epidemiology, survival analysis, and bioinformatics. (pp. 365-383) edited by Atanu Biswas, Sujay Datta, Jason P. Fine and Mark R. Segal. Hoboken, NJ, United States: John Wiley & Sons. doi: 10.1002/9780470181218.ch21
Correcting for Selection Bias via Cross-Validation in the Classification of Microarray Data
McLachlan, G J., Chevelu, J. and Zhu, J. (2008). Correcting for Selection Bias via Cross-Validation in the Classification of Microarray Data. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen. (pp. 364-376) edited by Balakrishnan, N., Pena, E. A. and Silvapulle, M. J.. United States: Institute of Mathematical Statistics. doi: 10.1214/193940307000000284
Jones, L., Ng, S., Ambroise, C, Monico, K. A., Khan, N. and McLachlan, G. J. (2005). Use of microarray data via model-based classification in the study and prediction of survival from lung cancer. Methods of microarray data analysis IV. (pp. 163-173) edited by Jennifer S. Shoemaker and Simon M. Lin. New York, USA: Springer. doi: 10.1007/0-387-23077-7_13
Ng, S. K., Krishnan, T. and McLachlan, G. J. (2004). The EM algorithm. Handbook of Computational Statistics: Concepts and Methods. (pp. 137-168) edited by J.E. Gentle, W. Hardle and Y. Mori. Germany: Springer-Verlag.
On clustering by mixture models
McLachlan, G. J., Ng, A.S. K. and Peel, D. (2003). On clustering by mixture models. Exploratory Data Analysis in Empirical Research. (pp. 141-148) edited by M. Schwaiger and O. Opitz. Germany: Springer. doi: 10.1007/978-3-642-55721-7_16
Functional mixtures-of-experts
Chamroukhi, Faïcel, Pham, Nhat Thien, Hoang, Van Hà and McLachlan, Geoffrey J. (2024). Functional mixtures-of-experts. Statistics and Computing, 34 (3) 98. doi: 10.1007/s11222-023-10379-0
Semi‐supervised Gaussian mixture modelling with a missing‐data mechanism in R
Lyu, Ziyang, Ahfock, Daniel, Thompson, Ryan and McLachlan, Geoffrey J. (2024). Semi‐supervised Gaussian mixture modelling with a missing‐data mechanism in R. Australian and New Zealand Journal of Statistics, 66 (2), 146-162. doi: 10.1111/anzs.12413
An overview of skew distributions in model-based clustering
Lee, Sharon X. and McLachlan, Geoffrey J. (2024). An overview of skew distributions in model-based clustering. Science Talks, 9 100298, 100298. doi: 10.1016/j.sctalk.2024.100298
Koh, Edwin J.Y., Amini, Eiman, Spier, Carlos A., McLachlan, Geoffrey J., Xie, Weiguo and Beaton, Nick (2024). A mineralogy characterisation technique for copper ore in flotation pulp using deep learning machine vision with optical microscopy. Minerals Engineering, 205 108481, 1-16. doi: 10.1016/j.mineng.2023.108481
A new algorithm for support vector regression with automatic selection of hyperparameters
Wang, You-Gan, Wu, Jinran, Hu, Zhi-Hua and McLachlan, Geoffrey J. (2023). A new algorithm for support vector regression with automatic selection of hyperparameters. Pattern Recognition, 133 108989, 1-9. doi: 10.1016/j.patcog.2022.108989
Koh, Edwin J. Y., Amini, Eiman, Gaur, Shruti, Becerra Maquieira, Miguel, Jara Heck, Christian, McLachlan, Geoffrey J. and Beaton, Nick (2022). An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models. Minerals Engineering, 189 107886, 107886. doi: 10.1016/j.mineng.2022.107886
Order selection with confidence for finite mixture models
Nguyen, Hien D., Fryer, Daniel and McLachlan, Geoffrey J. (2022). Order selection with confidence for finite mixture models. Journal of the Korean Statistical Society, 52 (1), 154-184. doi: 10.1007/s42952-022-00195-z
A spatial heterogeneity mixed model with skew-elliptical distributions
Farzammehr, Mohadeseh Alsadat and McLachlan, Geoffrey J. (2022). A spatial heterogeneity mixed model with skew-elliptical distributions. Communications for Statistical Applications and Methods, 29 (3), 373-391. doi: 10.29220/csam.2022.29.3.373
Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces
Nguyen, TrungTin, Chamroukhi, Faicel, Nguyen, Hien D. and McLachlan, Geoffrey J. (2022). Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. Communications in Statistics - Theory and Methods, 52 (14), 1-12. doi: 10.1080/03610926.2021.2002360
Statistical file-matching of non-Gaussian data: a game theoretic approach
Ahfock, Daniel, Pyne, Saumyadipta and McLachlan, Geoffrey J. (2022). Statistical file-matching of non-Gaussian data: a game theoretic approach. Computational Statistics and Data Analysis, 168 107387, 1-16. doi: 10.1016/j.csda.2021.107387
Semi-supervised learning of classifiers from a statistical perspective: a brief review
Ahfock, Daniel and McLachlan, Geoffrey J. (2022). Semi-supervised learning of classifiers from a statistical perspective: a brief review. Econometrics and Statistics, 26, 124-138. doi: 10.1016/j.ecosta.2022.03.007
An overview of skew distributions in model-based clustering
Lee, Sharon X. and McLachlan, Geoffrey J. (2022). An overview of skew distributions in model-based clustering. Journal of Multivariate Analysis, 188 104853, 1-14. doi: 10.1016/j.jmva.2021.104853
Nguyen, Hien Duy, Nguyen, TrungTin, Chamroukhi, Faicel and McLachlan, Geoffrey John (2021). Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models. Journal of Statistical Distributions and Applications, 8 (1) 13. doi: 10.1186/s40488-021-00125-0
Ng, Shu Kay, Tawiah, Richard, McLachlan, Geoffrey J. and Gopalan, Vinod (2021). Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach. Biostatistics, 24 (1), 108-123. doi: 10.1093/biostatistics/kxab037
Koh, Edwin J. Y., Amini, Eiman, McLachlan, Geoffrey J. and Beaton, Nick (2021). Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy. Minerals Engineering, 173 107230, 107230. doi: 10.1016/j.mineng.2021.107230
Robust clustering based on finite mixture of multivariate fragmental distributions
Maleki, Mohsen, McLachlan, Geoffrey J. and Lee, Sharon X. (2021). Robust clustering based on finite mixture of multivariate fragmental distributions. Statistical Modelling, 23 (3), 1-26. doi: 10.1177/1471082X211048660
Koh, Edwin J.Y., Amini, Eiman, McLachlan, Geoffrey J. and Beaton, Nick (2021). Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations. Minerals Engineering, 170 107026, 1-11. doi: 10.1016/j.mineng.2021.107026
Data fusion using factor analysis and low-rank matrix completion
Ahfock, Daniel, Pyne, Saumyadipta and McLachlan, Geoffrey J. (2021). Data fusion using factor analysis and low-rank matrix completion. Statistics and Computing, 31 (5) 58. doi: 10.1007/s11222-021-10033-7
Multi‐node expectation–maximization algorithm for finite mixture models
Lee, Sharon X., McLachlan, Geoffrey J. and Leemaqz, Kaleb L. (2021). Multi‐node expectation–maximization algorithm for finite mixture models. Statistical Analysis and Data Mining: The ASA Data Science Journal, 14 (4) sam.11529, 297-304. doi: 10.1002/sam.11529
Farzammehr, M. A., Mohammadzadeh, M, Zadkarami, M. R. and McLachlan, G. J. (2021). Bayesian analysis of generalized linear mixed models with spatial correlated and unrestricted skew normal errors. Communications in Statistics: Theory and Methods, 51 (24), 1-22. doi: 10.1080/03610926.2021.1897843
Harmless label noise and informative soft-labels in supervised classification
Ahfock, Daniel and McLachlan, Geoffrey J. (2021). Harmless label noise and informative soft-labels in supervised classification. Computational Statistics and Data Analysis, 161 107253, 107253. doi: 10.1016/j.csda.2021.107253
On formulations of skew factor models: Skew factors and/or skew errors
Lee, Sharon X. and McLachlan, Geoffrey J. (2021). On formulations of skew factor models: Skew factors and/or skew errors. Statistics and Probability Letters, 168 108935, 108935. doi: 10.1016/j.spl.2020.108935
Ahfock, Daniel and McLachlan, Geoffrey J. (2020). An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classified. Statistics and Computing, 30 (6), 1779-1790. doi: 10.1007/s11222-020-09971-5
Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions
Lee, Sharon X., Lin, Tsung-I and McLachlan, Geoffrey J. (2020). Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions. Advances in Data Analysis and Classification, 15 (2), 481-512. doi: 10.1007/s11634-020-00420-9
A Mixture of Regressions Model of COVID-19 Death Rates and Population Comorbidities
Maleki, M. , McLachlan, G. J. , Gurewitsch, R. , Aruru, M. and Pyne, S. (2020). A Mixture of Regressions Model of COVID-19 Death Rates and Population Comorbidities. Statistics and Applications, 18 (1), 295-306.
Approximation by finite mixtures of continuous density functions that vanish at infinity
Nguyen, T. Tin, Nguyen, Hien D., Chamroukhi, Faicel and McLachlan, Geoffrey J. (2020). Approximation by finite mixtures of continuous density functions that vanish at infinity. Cogent Mathematics and Statistics, 7 (1). doi: 10.1080/25742558.2020.1750861
Mini-batch learning of exponential family finite mixture models
Nguyen, Hien D., Forbes, Florence and McLachlan, Geoffrey J. (2020). Mini-batch learning of exponential family finite mixture models. Statistics and Computing, 30 (4), 731-748. doi: 10.1007/s11222-019-09919-4
Tawiah, Richard, McLachlan, Geoffrey J. and Ng, Shu Kay (2020). A bivariate joint frailty model with mixture framework for survival analysis of recurrent events with dependent censoring and cure fraction. Biometrics, 76 (3) biom.13202, 753-766. doi: 10.1111/biom.13202
On approximations via convolution-defined mixture models
Nguyen, Hien D. and McLachlan, Geoffrey (2019). On approximations via convolution-defined mixture models. Communications in Statistics - Theory and Methods, 48 (16), 3945-3955. doi: 10.1080/03610926.2018.1487069
Nguyen, Hien D., Yee, Yohan, McLachlan, Geoffrey J. and Lerch, Jason P. (2019). False discovery rate control for grouped or discretely supported p-values with application to a neuroimaging study. SORT, 43 (2), 1-22. doi: 10.2436/20.8080.02.87
A multilevel survival model with random covariates and unobservable random effects
Tawiah, Rchard, Yau, Kelvin K. W., McLachlan, Geoffrey J., Chambers, Suzanne and Ng, Shu-Kay (2019). A multilevel survival model with random covariates and unobservable random effects. Statistics in Medicine, 38 (6), 1036-1055. doi: 10.1002/sim.8041
McLachlan, Geoffrey J., Lee, Sharon X. and Rathnayake, Suren I. (2019). Finite mixture models. Annual Review of Statistics and Its Application, 6 (1), 355-378. doi: 10.1146/annurev-statistics-031017-100325
Mixture cure models with time-varying and multilevel frailties for recurrent event data
Tawiah, Richard, McLachlan, Geoffrey J. and Ng, Shu Kay (2019). Mixture cure models with time-varying and multilevel frailties for recurrent event data. Statistical Methods in Medical Research, 29 (5) ARTN 0962280219859377, 096228021985937-1385. doi: 10.1177/0962280219859377
Skew-normal Bayesian spatial heterogeneity panel data models
Farzammehr, Mohadeseh Alsadat, Zadkarami, Mohammad Reza, McLachlan, Geoffrey J. and Lee, Sharon X. (2019). Skew-normal Bayesian spatial heterogeneity panel data models. Journal of Applied Statistics, 47 (5), 1-23. doi: 10.1080/02664763.2019.1657812
Skew-normal generalized spatial panel data model
Farzammehr, Mohadeseh Alsadat, Zadkarami, Mohammad Reza and McLachlan, Geoffrey J. (2019). Skew-normal generalized spatial panel data model. Communications in Statistics: Simulation and Computation, 50 (11), 1-29. doi: 10.1080/03610918.2019.1622718
Ng, Shu-Kay, Tawiah, Richard and McLachlan, Geoffrey J. (2018). Unsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling framework. Pattern Recognition, 88, 261-271. doi: 10.1016/j.patcog.2018.11.022
Randomized mixture models for probability density approximation and estimation
Nguyen, Hien D., Wang, Dianhui and McLachlan, Geoffrey J. (2018). Randomized mixture models for probability density approximation and estimation. Information Sciences, 467, 135-148. doi: 10.1016/j.ins.2018.07.056
logKDE: log-transformed kernel density estimation
Jones, Andrew T., Nguyen, Hien D. and McLachlan, Geoffrey J. (2018). logKDE: log-transformed kernel density estimation. Journal of Open Source Software, 3 (28) 870, 870. doi: 10.21105/joss.00870
Stream-suitable optimization algorithms for some soft-margin support vector machine variants
Nguyen, Hien D., Jones, Andrew T. and McLachlan, Geoffrey J. (2018). Stream-suitable optimization algorithms for some soft-margin support vector machine variants. Japanese Journal of Statistics and Data Science., 1 (1), 81-108. doi: 10.1007/s42081-018-0001-y
A Block EM Algorithm for Multivariate Skew Normal and Skew t-Mixture Models
Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2018). A Block EM Algorithm for Multivariate Skew Normal and Skew t-Mixture Models. IEEE Transactions on Neural Networks and Learning Systems, 29 (99) 8310916, 1-11. doi: 10.1109/TNNLS.2018.2805317
A globally convergent algorithm for a lasso-penalized mixture of linear regression models
Lloyd-Jones, Luke R., Nguyen, Hien D. and McLachlan, Geoffrey J. (2018). A globally convergent algorithm for a lasso-penalized mixture of linear regression models. Computational Statistics and Data Analysis, 119, 19-38. doi: 10.1016/j.csda.2017.09.003
Chunked-and-averaged estimators for vector parameters
Nguyen, Hien D. and McLachlan, Geoffrey J. (2018). Chunked-and-averaged estimators for vector parameters. Statistics and Probability Letters, 137, 336-342. doi: 10.1016/j.spl.2018.02.051
EMMIXcskew: an R package for the fitting of a mixture of canonical fundamental skew t-distributions
Lee, Sharon X. and McLachlan, Geoffrey J. (2018). EMMIXcskew: an R package for the fitting of a mixture of canonical fundamental skew t-distributions. Journal of Statistical Software, 83 (3). doi: 10.18637/jss.v083.i03
Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modeling
Nguyen, Hien D., Ullmann, Jeremy F. P., Mclachlan, Geoffrey J., Voleti, Venkatakaushik, Li, Wenze, Hillman, Elizabeth M. C., Reutens, David C. and Janke, Andrew L. (2017). Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modeling. Statistical Analysis and Data Mining, 11 (1), 5-16. doi: 10.1002/sam.11366
Viroli, Cinzia and McLachlan, Geoffrey J. (2017). Deep Gaussian mixture models. Statistics and Computing, 29 (1), 1-9. doi: 10.1007/s11222-017-9793-z
Some theoretical results regarding the polygonal distribution
Nguyen, Hien D. and McLachlan, Geoffrey J. (2017). Some theoretical results regarding the polygonal distribution. Communications in Statistics: Theory and Methods, 47 (20), 5083-5095. doi: 10.1080/03610926.2017.1386312
Finite mixture models in biostatistics
Lee, Sharon X., Ng, Shu-Kay and McLachlan, Geoffrey J. (2017). Finite mixture models in biostatistics. Handbook of Statistics, 36, 75-102.
Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution
Lin, Tsung-I, Wang, Wan-Lun, McLachlan, Geoffrey J. and Lee, Sharon X. (2017). Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution. Statistical Modelling, 18 (1), 50-72. doi: 10.1177/1471082X17718119
Maximum pseudolikelihood estimation for model-based clustering of time series data
Nguyen, Hien D., McLachlan, Geoffrey J., Orban, Pierre, Bellec, Pierre and Janke, Andrew L. (2017). Maximum pseudolikelihood estimation for model-based clustering of time series data. Neural Computation, 29 (4), 990-1020. doi: 10.1162/NECO_a_00938
A universal approximation theorem for mixture-of-experts models
Nguyen, Hien D., Lloyd-Jones, Luke R. and McLachlan, Geoffrey J. (2016). A universal approximation theorem for mixture-of-experts models. Neural Computation, 28 (12), 2585-2593. doi: 10.1162/NECO_a_00892
Lloyd-Jones, Luke R., Nguyen, Hien D., Mclachlan, Geoﬀrey J., Sumpton, Wayne and Wang, You-Gan (2016). Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics, 72 (4), 1255-1265. doi: 10.1111/biom.12531
Partial identification in the statistical matching problem
Ahfock, Daniel, Pyne, Saumyadipta, Lee, Sharon X. and McLachlan, Geoffrey J. (2016). Partial identification in the statistical matching problem. Computational Statistics and Data Analysis, 104, 79-90. doi: 10.1016/j.csda.2016.06.005
Progress on a conjecture regarding the triangular distribution
Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Progress on a conjecture regarding the triangular distribution. Communications in Statistics: Theory and Methods, 46 (22), 11261-11271. doi: 10.1080/03610926.2016.1263742
Linear mixed models with marginally symmetric nonparametric random effects
Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Linear mixed models with marginally symmetric nonparametric random effects. Computational Statistics and Data Analysis, 103, 151-169. doi: 10.1016/j.csda.2016.05.005
Spatial clustering of time series via mixture of autoregressions models and Markov random fields
Nguyen, Hien D., McLachlan, Geoffrey J., Ullmann, Jeremy F. P. and Janke, Andrew L. (2016). Spatial clustering of time series via mixture of autoregressions models and Markov random fields. Statistica Neerlandica, 70 (4), 414-439. doi: 10.1111/stan.12093
Maximum likelihood estimation of triangular and polygonal distributions
Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Maximum likelihood estimation of triangular and polygonal distributions. Computational Statistics and Data Analysis, 102, 23-36. doi: 10.1016/j.csda.2016.04.003
McLachlan, Geoffrey J. and Lee, Sharon X. (2016). Comment on "On nomenclature for, and the relative merits of, two formulations of skew distributions," by A. Azzalini, R. Browne, M. Genton, and P. McNicholas. Statistics & Probability Letters, 116, 1-5. doi: 10.1016/j.spl.2016.04.004
A block minorization-maximization algorithm for heteroscedastic regression
Nguyen, Hien D., Lloyd-Jones, Luke R. and McLachlan, Geoffrey J. (2016). A block minorization-maximization algorithm for heteroscedastic regression. IEEE Signal Processing Letters, 23 (8) 7501879, 1131-1135. doi: 10.1109/LSP.2016.2586180
Lee, Sharon X and McLachlan, Geoffrey J (2016). Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture models. Statistics and Computing, 26 (3), 573-589. doi: 10.1007/s11222-015-9545-x
Laplace mixture autoregressive models
Nguyen, Hien D., McLachlan, Geoffrey J., Ullmann, Jeremy F. P. and Janke, Andrew L. (2016). Laplace mixture autoregressive models. Statistics and Probability Letters, 110, 18-24. doi: 10.1016/j.spl.2015.11.006
Aghaeepour, Nima, Chattopadhyay, Pratip, Chikina, Maria, Dhaene, Tom, Van Gassen, Sofie, Kursa, Miron, Lambrecht, Bart N., Malek, Mehrnoush, McLachlan, G. J., Qian, Yu, Qiu, Peng, Saeys, Yvan, Stanton, Rick, Tong, Dong, Vens, Celine, Walkowiak, Slawomir, Wang, Kui, Finak, Greg, Gottardo, Raphael, Mosmann, Tim, Nolan, Garry P., Scheuermann, Richard H. and Brinkman, Ryan R. (2016). A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry Part A, 89 (1), 16-21. doi: 10.1002/cyto.a.22732
Extending mixtures of factor models using the restricted multivariate skew-normal distribution
Lin, Tsung-I, McLachlan, Geoffrey J. and Lee, Sharon X. (2016). Extending mixtures of factor models using the restricted multivariate skew-normal distribution. Journal of Multivariate Analysis, 143, 398-413. doi: 10.1016/j.jmva.2015.09.025
Laplace mixture of linear experts
Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Laplace mixture of linear experts. Computational Statistics and Data Analysis, 93, 177-191. doi: 10.1016/j.csda.2014.10.016
Mixtures of spatial spline regressions for clustering and classification
Nguyen, Hien D., McLachlan, Geoffrey J. and Wood, Ian A. (2016). Mixtures of spatial spline regressions for clustering and classification. Computational Statistics and Data Analysis, 93, 76-85. doi: 10.1016/j.csda.2014.01.011
Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure. Cytometry Part A, 89 (1), 30-43. doi: 10.1002/cyto.a.22789
Tian, Ting, McLachlan, Geoffrey J., Dieter, Mark J. and Basford, Kaye E. (2015). Application of multiple imputation for missing values in three-way three-mode multi-environment trial data. PLoS One, 10 (12) e0144370, e0144370.1-e0144370.25. doi: 10.1371/journal.pone.0144370
Special issue on "New trends on model-based clustering and classification"
Ingrassia, Salvatore, McLachlan, Geoffrey J. and Govaert, Gerard (2015). Special issue on "New trends on model-based clustering and classification". Advances in Data Analysis and Classification, 9 (4), 367-369. doi: 10.1007/s11634-015-0224-8
Maximum likelihood estimation of Gaussian mixture models without matrix operations
Nguyen, Hien D. and McLachlan, Geoffrey J. (2015). Maximum likelihood estimation of Gaussian mixture models without matrix operations. Advances in Data Analysis and Classification, 9 (4), 371-394. doi: 10.1007/s11634-015-0209-7
Inference on differences between classes using cluster-specific contrasts of mixed effects
Ng, Shu Kay, McLachlan, Geoffrey J., Wang, Kui, Nagymanyoki, Zoltan, Liu, Shubai and Ng, Shu-Wing (2015). Inference on differences between classes using cluster-specific contrasts of mixed effects. Biostatistics, 16 (1), 98-112. doi: 10.1093/biostatistics/kxu028
Nature and man: the goal of bio-security in the course of rapid and inevitable human development
Pyne, Saumyadipta, Lee, Sharon X. and McLachlan, Geoffrey J. (2015). Nature and man: the goal of bio-security in the course of rapid and inevitable human development. Journal of the Indian Society of Agricultural Statistics, 69 (2), 117-125.
A robust factor analysis model using the restricted skew-t distribution
Lin, Tsung-I, Wu, Pal H., McLachlan, Geoffrey J. and Lee, Sharon X. (2014). A robust factor analysis model using the restricted skew-t distribution. Test, 24 (3), 510-531. doi: 10.1007/s11749-014-0422-2
On the number of components in a Gaussian mixture model
McLachlan, Geoffrey J. and Rathnayake, Suren (2014). On the number of components in a Gaussian mixture model. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, 4 (5), 341-355. doi: 10.1002/widm.1135
Pyne, Saumyadipta, Lee, Sharon X., Wang, Kui, Irish, Jonathan, Tamayo, Pablo, Nazaire, Marc-Danie, Duong, Tarn, Ng, Shu-Kay, Hafler, David, Levy, Ronald, Nolan, Garry P., Mesirov, Jill and McLachlan, Geoffrey J. (2014). Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. PLoS One, 9 (7) e100334, e100334.1-e100334.11. doi: 10.1371/journal.pone.0100334
False discovery rate control in magnetic resonance imaging studies via Markov random fields
Nguyen, Hien D., McLachlan, Geoffrey J., Cherbuin, Nicolas and Janke, Andrew L. (2014). False discovery rate control in magnetic resonance imaging studies via Markov random fields. IEEE Transactions on Medical Imaging, 33 (8) 6811158, 1735-1748. doi: 10.1109/TMI.2014.2322369
Finite mixtures of multivariate skew t-distributions: Some recent and new results
Lee, Sharon and McLachlan, Geoffrey J. (2014). Finite mixtures of multivariate skew t-distributions: Some recent and new results. Statistics and Computing, 24 (2), 181-202. doi: 10.1007/s11222-012-9362-4
Mixture models for clustering multilevel growth trajectories
Ng S.K. and McLachlan G.J. (2014). Mixture models for clustering multilevel growth trajectories. Computational Statistics and Data Analysis, 71, 43-51. doi: 10.1016/j.csda.2012.12.007
The 2nd special issue on advances in mixture models
Boehning, Dankmar, Hennig, Christian, McLachlan, Geoffrey J. and McNicholas, Paul D. (2014). The 2nd special issue on advances in mixture models. Computational Statistics and Data Analysis, 71, 1-2. doi: 10.1016/j.csda.2013.10.010
Lee S.X. and McLachlan G.J. (2013). EMMIXuskew: An R package for Fitting Mixtures of Multivariate Skew t distributions via the EM algorithm. Journal of Statistical Software, 55 (12), 1-22. doi: 10.18637/jss.v055.i12
Model-based clustering and classification with non-normal mixture distributions
Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Model-based clustering and classification with non-normal mixture distributions. Statistical Methods and Applications, 22 (4), 427-454. doi: 10.1007/s10260-013-0237-4
Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions". Statistical Methods and Applications, 22 (4), 473-479. doi: 10.1007/s10260-013-0249-0
On mixtures of skew normal and skew t-distributions
Lee, Sharon X. and McLachlan, Geoffrey J. (2013). On mixtures of skew normal and skew t-distributions. Advances in Data Analysis and Classification, 7 (3), 241-266. doi: 10.1007/s11634-013-0132-8
McLachlan, G. J. (2013). How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification: written contribution to the discussion on the paper by Hennig and Liao. Applied Statistics-Journal of the Royal Statistical Society Series C, 62 (3), 309-369. doi: 10.1111/j.1467-9876.2012.01066.x
Critical assessment of automated flow cytometry analysis techniques
Aghaeepour, Nima, Finak, Greg, Hoos, Holger, Mosmann, Tim R., Brinkman, Ryan, Gottardo, Raphael, Scheuermann, Richard H., The FlowCAP Consortium, McLachlan, Geoffrey J., Wang, Kui and The DREAM Consortium (2013). Critical assessment of automated flow cytometry analysis techniques. Nature Methods, 10 (3), 228-238. doi: 10.1038/nmeth.2365
On the classification of microarray gene-expression data
Basford, Kaye E., McLachlan, Geoffrey J. and Rathnayake, Suren I. (2013). On the classification of microarray gene-expression data. Briefings in Bioinformatics, 14 (4) bbs056, 402-410. doi: 10.1093/bib/bbs056
Wang, Kui, Ng, Shu Kay and McLachlan, Geoffrey J. (2012). Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects. Bmc Bioinformatics, 13 (1) 300, 300.1-300.14. doi: 10.1186/1471-2105-13-300
McLachlan, Geoffrey J. (2012). Discriminant analysis. Wiley Interdisciplinary Reviews: Computational Statistics., 4 (5), 421-431. doi: 10.1002/wics.1219
Schroder, Kate, Irvine, Katharine M., Taylor, Martin S., Bokil, Nilesh J., Le Cao, Kim-Anh, Masterman, Kelly-Anne, Labzin, Larisa I., Semple, Colin A., Kapetanovic, Ronan, Fairbairn, Lynsey, Akalin, Altuna, Faulkner, Geoffrey J., Baillie, John Kenneth, Gongora, Milena, Daub, Carsten O., Kawaji, Hideya, McLachlan, Geoffrey J., Goldman, Nick, Grimmond, Sean M., Carninci, Piero, Suzuki, Harukazu, Hayashizaki, Yoshihide, Lenhard, Boris, Hume, David A. and Sweet, Matthew J. (2012). Conservation and divergence in Toll-like receptor 4-regulated gene expression in primary human versus mouse macrophages. Proceedings of the National Academy of Sciences of the USA, 109 (16), E944-E953. doi: 10.1073/pnas.1110156109
Top-10 data mining case studies
Melli, Gabor, Wu, Xindong, Beinat, Paul, Bonchi, Francesco, Cao, Longbing, Duan, Rong, Faloutsos, Christos, Ghani, Rayid, Kitts, Brendan, Goethals, Bart, McLachlan, Geoff, Pei, Jian, Srivastava, Ashok and Zaiane, Osmar (2012). Top-10 data mining case studies. International Journal of Information Technology and Decision Making, 11 (2), 389-400. doi: 10.1142/S021962201240007X
A very fast algorithm for matrix factorization
Nikulin, V, Huang, TH, Ng, SK, Rathnayake, SI and McLachlan, GJ (2011). A very fast algorithm for matrix factorization. Statistics and Probability Letters, 81 (7), 773-782. doi: 10.1016/j.spl.2011.02.001
Mixtures of common t-factor analyzers for clustering high-dimensional microarray data
Baek, Jangsun and McLachlan, Geoffrey J. (2011). Mixtures of common t-factor analyzers for clustering high-dimensional microarray data. Bioinformatics, 27 (9) btr112, 1269-1276. doi: 10.1093/bioinformatics/btr112
Nikulin, Vladimir, Huang, Tian-Hsiang and McLachlan, Geoffrey J. (2011). Classification of high-dimensional microarray data with a two-step procedure via a Wilcoxon criterion and multilayer perceptron. International Journal of Computational Intelligence and Applications, 10 (1), 1-14. doi: 10.1142/S1469026811002969
Commentary on Steinley and Brusco (2011): Recommendations and cautions
McLachlan, Geoffrey J. (2011). Commentary on Steinley and Brusco (2011): Recommendations and cautions. Psychological Methods, 16 (1), 80-81. doi: 10.1037/a0021141
Assessing the adequacy of Weibull survival models: a simulated envelope approach
Zhao, Yun, Lee, Andy H., Yau, Kelvin K.W. and McLachlan, Geoffrey J. (2011). Assessing the adequacy of Weibull survival models: a simulated envelope approach. Journal of Applied Statistics, 38 (10), 2089-2097. doi: 10.1080/02664763.2010.545115
Testing for Group Structure in High-Dimensional Data
McLachlan, G. J. and Rathnayake, S. I. (2011). Testing for Group Structure in High-Dimensional Data. Journal of Biopharmaceutical Statistics, 21 (6), 1113-1125. doi: 10.1080/10543406.2011.608342
Baek, Jangsun, McLachlan, Geoffrey J. and Flack, Lloyd K. (2010). Mixtures of factor analyzers with common factor loadings: Applications to the clustering and visualization of high-dimensional data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (7) 5184847, 1298-1309. doi: 10.1109/TPAMI.2009.149
Integrative mixture of experts to combine clinical factors and gene markers
Le Cao, Kim-Anh, Meugnier, Emmanuelle and McLachlan, Geoffrey J. (2010). Integrative mixture of experts to combine clinical factors and gene markers. Bioinformatics, 26 (9) btq107, 1192-1198. doi: 10.1093/bioinformatics/btq107
Autoantibody profiling to identify biomarkers of key pathogenic pathways in mucinous ovarian cancer
Tang, Liangdan, Yang, Junzheng, Ng, Shu-Kay, Rodriguez, Noah, Choi, Pui-Wah, Vitonis, Allison, Wang, Kui, McLachlan, Geoffrey J., Caiazzo, Robert J., Liu, Brian C.-S., Welch, Brian C.-S., Cramer, Daniel W., Berkowitz, Ross S. and Ng, Shu-Wing (2010). Autoantibody profiling to identify biomarkers of key pathogenic pathways in mucinous ovarian cancer. European Journal of Cancer, 46 (1), 170-179. doi: 10.1016/j.ejca.2009.10.003
A score test for assessing the cured proportion in the long-term survivor mixture model
Zhao, Yun, Lee, Andy H., Yau, Kelvin K. W., Burke, Valerie and McLachlan, Geoffrey J. (2009). A score test for assessing the cured proportion in the long-term survivor mixture model. Statistics In Medicine, 28 (27), 3454-3466. doi: 10.1002/sim.3696
Automated high-dimensional flow cytometric data analysis
Pyne, S., Hu, X., Wang, K., Rossin, E., Lin, T.-I., Maier, L. M., Baecher-Allan, C., McLachlan, G. J., Tamayo, P., Hafler, D. A., De Jager, P. L. and Mesirow, J. P. (2009). Automated high-dimensional flow cytometric data analysis. Proceedings of the National Academy of Sciences of the United States of America, 106 (21), 8519-8524. doi: 10.1073/pnas.0903028106
Classification of imbalanced marketing data with balanced random sets
Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Classification of imbalanced marketing data with balanced random sets. Journal of Machine Learning Research, 7, 89-100.
Microarray data analysis for differential expression: a tutorial
Suarez, E., Burguete, A. and McLachlan, G. J. (2009). Microarray data analysis for differential expression: a tutorial. Puerto Rico Health Sciences Journal, 28 (2), 89-104.
Wallace's approach to unsupervised learning: The Snob program
Jorgensen, Murray A. and McLachlan, Geoffrey J. (2008). Wallace's approach to unsupervised learning: The Snob program. The Computer Journal, 51 (5), 571-578. doi: 10.1093/comjnl/bxm121
McLaren, C. E., Gordeuk, V. R., Chen, W. -P., Barton, J. C., Action, R. T., Speechley, M., Castro, O., Adams, P. C., Snively, B. M., Harris, E. L., Reboussin, D. M., McLachlan, G. J. and Bean, R. (2008). Bivariate mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African Americans, Hispanics, and whites in the Hemochromatosis and Iron Overload Screening (HEIRS) Study. Translational Research, 151 (2), 97-109. doi: 10.1016/j.trsl.2007.10.002
Characteristic Traffic Load Effects from a Mixture of Loading Events on Short to Medium Span Bridges
Caprani, C. C., O'Brien, E. J. and McLachlan, G. J. (2008). Characteristic Traffic Load Effects from a Mixture of Loading Events on Short to Medium Span Bridges. Structural Safety, 30 (3), 394-404. doi: 10.1016/j.strusafe.2006.11.006
Comments on: Augmenting the bootstrap to analyze high dimensional genomic data
McLachlan, Geoffrey J., Wang, K. and Ng, S. K. (2008). Comments on: Augmenting the bootstrap to analyze high dimensional genomic data. Test, 17 (1), 43-46. doi: 10.1007/s11749-008-0106-x
McLachlan, Geoff J., Wang, Kent and Ng, Shu Kay (2008). Large-scale simultaneous inference with applications to the detection of differential expression with microarray data (with discussion). Statistica, 68 (1), 1-30. doi: 10.6092/issn.1973-2201/3525
On selection biases with prediction rules formed from gene expression data
Zhu, J. X., McLachlan, G. J., Jones, L. B. T. and Wood, I. A. (2008). On selection biases with prediction rules formed from gene expression data. Journal of Statistical Planning and Inference, 138 (2), 374-386. doi: 10.1016/j.jspi.2007.06.003
Professor Gopal Kanji's retirement as editor of Journal of Applied Statistics
Agrawal, M. C., Caudill, Steven B., Chakraborti, S., Draper, Norman, Dryden, Ian, Gani, Joe, Gilmour, Steven G., Govindarajulu, Z., Hand, David J., Franses, Philip Hans, Kacker, Raghu, Khamis, Harry, Khuri, Andre I., Lewis, Toby, Mardia, Kanti, McLachlan, Geoff, Naik, Dayanand, Prescott, Phil, Kumar, V. S. Sampath, Tomizawa, Sadao and Wynn, Henry (2008). Professor Gopal Kanji's retirement as editor of Journal of Applied Statistics. Journal of Applied Statistics, 35 (1), 1-8. doi: 10.1080/02664760701814495
Top 10 Algorithms in Data Mining
Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z. H., Steinbach, M., Hand, D. J. and Steinberg, D. (2008). Top 10 Algorithms in Data Mining. Knowledge and Information Systems, 14 (1), 1-37. doi: 10.1007/s10115-007-0114-2
Two-component Poisson Mixture Regression Modelling of Count Data With Bivariate Random Effects
Wang, Kui, Yau, Kelvin K. W., Lee, Andy H. and McLachlan, Geoffrey J. (2007). Two-component Poisson Mixture Regression Modelling of Count Data With Bivariate Random Effects. Mathematical and Computer Modelling, 46 (11-12), 1468-1476. doi: 10.1016/j.mcm.2007.02.003
A Score Test for Overdispersion in Zero-Inflated Poisson Mixed Regression Model
Xiang, L., Lee, A. H., Yau, K. K. W. and McLachlan, G. J. (2007). A Score Test for Overdispersion in Zero-Inflated Poisson Mixed Regression Model. Statistics in Medicine, 26 (7), 1608-1622. doi: 10.1002/sim.2616
A tutorial in genetic epidemiology and some considerations in statistical modeling
Suarez, E., Sariol, C. A., Burguete, A. and McLachlan, G. J. (2007). A tutorial in genetic epidemiology and some considerations in statistical modeling. Puerto Rico Health Sciences Journal, 26 (4), 401-421.
Application of gene shaving and mixture models to cluster microarray gene expression data
Do, K. A., McLachlan, G. J., Bean, R. W. and Wen, S. (2007). Application of gene shaving and mixture models to cluster microarray gene expression data. Cancer Informatics, 5, 25-43. doi: 10.1177/117693510700500002
Extension of Mixture-of-Experts Networks for Binary Classification of Hierarchical Data
Ng, S. K. and McLachlan, G. J. (2007). Extension of Mixture-of-Experts Networks for Binary Classification of Hierarchical Data. Artificial Intelligence in Medicine, 41 (1), 57-67. doi: 10.1016/j.artmed.2007.06.001
Extension of the Mixture of Factor Analyzers Model to Incorporate the Multivariate t-Distribution
McLachlan, G. J., Bean, R. W. and Jones, L. B. T. (2007). Extension of the Mixture of Factor Analyzers Model to Incorporate the Multivariate t-Distribution. Computational Statistics & Data Analysis, 51 (11), 5327-5338. doi: 10.1016/j.csda.2006.09.015
Maternity length of stay modelling by Gamma mixture regression with random effects
Lee, Andy H., Wang, Kui, Yau, Kelvin K. W., McLachlan, Geoffrey J. and Ng, Shu Kay (2007). Maternity length of stay modelling by Gamma mixture regression with random effects. Biometrical Journal, 49 (5), 750-764. doi: 10.1002/bimj.200610371
Multilevel Survival Modelling of Recurrent Urinary Tract Infections
Wang, Kui, Yau, Kelvin K. W., Lee, Andy H. and McLachlan, Geoffrey J. (2007). Multilevel Survival Modelling of Recurrent Urinary Tract Infections. Computer Methods and Programs in Biomedicine, 87 (3), 225-229. doi: 10.1016/j.cmpb.2007.05.013
Lenzenweger, M. F., McLachlan, G. J. and Rubin, D. B. (2007). Resolving the latent structure of schizophrenia endophenotypes using expectation-maximization-based finite mixture modeling. Journal of Abnormal Psychology, 116 (1), 16-29. doi: 10.1037/0021-843X.116.1.16
Segmentation and intensity estimation of microarray images using a gamma-t mixture model
Baek, J., Son, Y. S. and McLachlan, G. J. (2007). Segmentation and intensity estimation of microarray images using a gamma-t mixture model. Bioinformatics, 23 (4), 458-465. doi: 10.1093/bioinformatics/btl630
Mixture models for detecting differentially expressed genes in microarrays
Jones, L. B. T., Bean, R., McLachlan, G. J. and Zhu, J. X. (2006). Mixture models for detecting differentially expressed genes in microarrays. International Journal of Neural Systems, 16 (5), 353-362. doi: 10.1142/S0129065706000755
A Score Test for Zero-Inflation in Correlated Count Data
Xiang, Liming, Lee, Andy H., Yau, Kelvin K. W. and McLachlan, Geoffrey J. (2006). A Score Test for Zero-Inflation in Correlated Count Data. Statistics In Medicine, 25 (10), 1660-1671. doi: 10.1002/sim.2308
A Mixture model with random-effects components for clustering correlated gene-expression profiles
Ng, SK, McLachlan, GJ, Wang, K, Jones, LBT and Ng, SW (2006). A Mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics, 22 (14), 1745-1752. doi: 10.1093/bioinformatics/btl165
McLachlan, GJ, Bean, RW and Jones, LBT (2006). A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics, 22 (13), 1608-1615. doi: 10.1093/bioinformatics/btl148
An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization
Ng, S. K., McLachlan, G. J. and Lee, A. H. (2006). An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization. Artificial Intelligence In Medicine, 36 (3), 257-267. doi: 10.1016/j.artmed.2005.07.003
Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros
Lee, AH, Wang, K, Scott, JA, Yau, KKW and McLachlan, GJ (2006). Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros. Statistical Methods In Medical Research, 15 (1), 47-61. doi: 10.1191/0962280206sm429oa
Robust cluster analysis via mixture models
McLachlan, G J, Ng, S K and Bean, R W (2006). Robust cluster analysis via mixture models. Austrian Journal of Statistics, 35 (2 & 3), 157-174.
Selection bias in working wit the top genes in supervised classification of tissue samples
Zhu, X., Ambroise, C and McLachlan, G J (2006). Selection bias in working wit the top genes in supervised classification of tissue samples. Statistical Methodology, 3 (1), 29-41. doi: 10.1016/j.stamet.2005.09.011
Cluster analysis of high-dimensional data: A case study
Bean, R and McLachlan, G (2005). Cluster analysis of high-dimensional data: A case study. Intelligent Data Engineering And Automated Learning Ideal 2005, Proceedings, 3578 (-), 302-310.
Kerr, R. J., McLachlan, G. J. and Henshall, J. M. (2005). Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis. Genetics Selection Evolution, 37 (1), 83-103. doi: 10.1051/gse:2004037
Using mixture models to detect differentially expressed genes
McLachlan, G. J., Bean, R. W., Jones, L. and Zhu, J. X. (2005). Using mixture models to detect differentially expressed genes. Australian Journal Of Experimental Agriculture, 45 (7-8), 859-866. doi: 10.1071/EA05051
Ng, S. K., McLachlan, G. J., Yau, K. K. W. and Lee, A. H. (2004). Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects Adjustment. Statistics In Medicine, 23 (17), 2729-2744. doi: 10.1002/sim.1840
Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images
Ng, Shu-Kay and McLachlan, Geoffrey J. (2004). Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognition, 37 (8), 1573-1589. doi: 10.1016/j.patcog.2004.02.012
Ng, S. K. and McLachlan, G. J. (2004). Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification. IEEE Transactions on Neural Networks, 15 (3), 738-749. doi: 10.1109/TNN.2004.826217
Clustering objects on subsets of attributes - Discussion
Hand, DJ, Glasbey, C, Husmeier, D, Gower, JC, van Houwelingen, HC, Bugrien, JB, Nason, G, Critchley, F, Hoff, PD, McLachlan, GJ and Bean, RW (2004). Clustering objects on subsets of attributes - Discussion. Journal of The Royal Statistical Society Series B-statistical Methodology, 66 (4), 839-849.
Mixture modelling for cluster analysis
McLachlan, G. J. and Chang, S. U. (2004). Mixture modelling for cluster analysis. Statistical Methods In Medical Research, 13 (5), 347-361. doi: 10.1191/0962280204sm372ra
McLachlan, GJ and Khan, N (2004). On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples. Journal of Multivariate Analysis, 90 (1), 90-105. doi: 10.1016/j.jmva.2004.02.002
Model-based clustering in gene expression microarrays: an application to breast cancer data
Mar, J.C. and McLachlan, G.J. (2003). Model-based clustering in gene expression microarrays: an application to breast cancer data. International Journal of Software Engineering and Knowledge Engineering, 13 (6), 579-592. doi: 10.1142/S0218194003001482
Model-based clustering in gene expression microarrays: an application to breast cancer data
Mar, J. C. and McLachlan, G. J. (2003). Model-based clustering in gene expression microarrays: an application to breast cancer data. International Journal of Software Engineering And Knowledge Engineering, 13 (6), 579-592. doi: 10.1142/S0218194003001482
Ng, S. K. and McLachlan, G. J. (2003). An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data. Statistics In Medicine, 22 (7), 1097-1111. doi: 10.1002/sim.1371
Ng, S. K. and McLachlan, G. J. (2003). On the Choice of the Number of Blocks with the Incremental EM Algorithm for the Fitting of Normal Mixtures. Statistics And Computing, 13 (1), 45-55. doi: 10.1023/A:1021987710829
Modelling High-Dimensional Data by Mixtures of Factor Analyzers
McLachlan, G. J., Peel, D. and Bean, R. W. (2003). Modelling High-Dimensional Data by Mixtures of Factor Analyzers. Computational Statistics & Data Analysis, 41 (3-4), 379-388. doi: 10.1016/S0167-9473(02)00183-4
On some variants of the EM algorithm for the fitting of finite mixture models
Ng, A.S. K. and McLachlan, G. J. (2003). On some variants of the EM algorithm for the fitting of finite mixture models. Austrian Journal of Statistics, 32 (1 & 2), 143-161.
Selection bias in gene extraction on the basis of microarray gene-expression data
Ambroise, Christophe and McLachlan, Geoffrey J. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences of the United States of America, 99 (10), 6562-6566. doi: 10.1073/pnas.102102699
A mixture model-based approach to the clustering of microarray expression data
McLachlan, GJ, Bean, RW and Peel, D (2002). A mixture model-based approach to the clustering of microarray expression data. Bioinformatics, 18 (3), 413-422. doi: 10.1093/bioinformatics/18.3.413
Ng, S. K., O'Brien, M. F., Harrocks, S. N. and McLachlan, G. J. (2002). Influence of patient age and implantation technique on the probability of re-replacement of the homograft aortic valve. Journal of Heart Valve Disease, 11 (2), 217-223.
Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data
Cadez, I. V., Smyth, P., McLachlan, G. J. and McLaren, C. E. (2002). Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. Machine Learning, 47 (1), 7-34. doi: 10.1023/A:1013679611503
McLachlan, G. J. and Hamaty, K. L. (2002). Nearest-neighbor variance estimation (NNVE): Robust covariance estimation via nearest-neighbor cleaning - Comment. Journal of The American Statistical Association, 97 (460), 1009-1011. doi: 10.1198/016214502388618807
MRI based diffusion and perfusion predictive model to estimate stroke evolution
Rose, Stephen E., Chalk, Jonathan B., Griffin, Mark P., Janke, Andrew L., Chen, Fang, Mclachlan, Geoffrey J., Peel, David, Zelaya, Fernando O., Markus, Hugh S., Jones, Derek K., Simmons, Andrew, O'Sullivan, Michael, Jarosz, Jo M., Strugnell, Wendy and Doddrell, David M. (2001). MRI based diffusion and perfusion predictive model to estimate stroke evolution. Magnetic Resonance Imaging, 19 (8), 1043-1053. doi: 10.1016/S0730-725X(01)00435-0
McLachlan, G. J. (2001). Letter to the editor. Journal of Agricultural, Biological, and Environmental Statistics, 6 (2), 302-304. doi: 10.1198/108571101750524797
Fitting mixtures of Kent distributions to aid in joint set identification
Peel, D, Whiten, WJ and McLachlan, GJ (2001). Fitting mixtures of Kent distributions to aid in joint set identification. Journal of The American Statistical Association, 96 (453), 56-63. doi: 10.1198/016214501750332974
Robust Mixture Modelling Using the t Distribution
Peel, D. and McLachlan, G. J. (2000). Robust Mixture Modelling Using the t Distribution. Statistics and Computing, 10 (4), 339-348. doi: 10.1023/A:1008981510081
Heterogeneity in schizophrenia; mixture modelling of age-at-first-admission, gender and diagnosis
Welham, J., McLachlan, G., Davies, G. and McGrath, J. (2000). Heterogeneity in schizophrenia; mixture modelling of age-at-first-admission, gender and diagnosis. Acta Psychiatrica Scandinavica, 101 (4), 312-317. doi: 10.1034/j.1600-0447.2000.101004312.x
Heterogeneity in schizophrenia; mixture modelling of age-at-first admission, gender and diagnosis
Welham, J., McLachlan, G. J., Davies, G. and McGrath, J. J. (2000). Heterogeneity in schizophrenia; mixture modelling of age-at-first admission, gender and diagnosis. Acta Pyschiatrica Scandinavica, 1, 312-317.
McLaren, C. E., Kambour, E. L., McLachlan, G. J., Lukaski, H. C., Li, X., Brittenham, G. M. and McLaren, G. D. (2000). Patient-specific Analysis of Sequential Haematological Data by Multiple Linear Regression and Mixture Distribution Modelling. Statistics in Medicine, 19 (1), 83-98. doi: 10.1002/(SICI)1097-0258(20000115)19:1<83::AID-SIM246>3.0.CO;2-A
The EMMIX software for the fitting of mixtures of normal and t-components
McLachlan, G. J., Peel, D., Basford, K. E. and Adams, P. (1999). The EMMIX software for the fitting of mixtures of normal and t-components. Journal of Statistical Software, 4 (2).
Constrained mixture models in competing risks problems
Ng, SK, McLachlan, GJ, McGiffin, DC and OBrien, MF (1999). Constrained mixture models in competing risks problems. Environmetrics, 10 (6), 753-767. doi: 10.1002/(SICI)1099-095X(199911/12)10:6<753::AID-ENV388>3.3.CO;2-B
25 years of applied statistics
McLachlan, G (1998). 25 years of applied statistics. Journal of Applied Statistics, 25 (1), 3-22.
McLarenCE, McLachlanGJ, HallidayJW, WebbSI, LeggettBA, JazwinskaEC, Crawford, DHG, GordeukVR, McLarenGD and PowellLW (1998). Distribution of transferrin saturation in an Australian population: Relevance to the early diagnosis of hemochromatosis. Gastroenterology, 114 (3), 543-549. doi: 10.1016/S0016-5085(98)70538-4
Heterogeneity in schizophrenia: A mixture model analysis based on age-of-onset, gender and diagnosis
McLachlan, G, Welham, J and McGrath, J (1998). Heterogeneity in schizophrenia: A mixture model analysis based on age-of-onset, gender and diagnosis. Schizophrenia Research, 29 (1-2), 25-25. doi: 10.1016/S0920-9964(97)88353-3
Mathematical classification and clustering.
McLachlan, G (1998). Mathematical classification and clustering.. Psychometrika, 63 (1), 93-95. doi: 10.1007/BF02295440
On modifications to the long-term survival mixture model in the presence of competing risks
Ng, SK and McLachlan, GJ (1998). On modifications to the long-term survival mixture model in the presence of competing risks. Journal of Statistical Computation And Simulation, 61 (1-2), 77-96. doi: 10.1080/00949659808811903
Basford, K. E., Mclachlan, G. J. and York, M. G. (1997). Modelling the distribution of stamp paper thickness via finite normal mixtures: The 1872 Hidalgo stamp issue of Mexico revisited. Journal of Applied Statistics, 24 (2), 169-179.
An algorithm for fitting mixtures of Gompertz distributions to censored survival data
McLachlan, G. J., Ng, S. K., Adams, P., McGiffin, D. C. and Galbraith, A. J. (1997). An algorithm for fitting mixtures of Gompertz distributions to censored survival data. Journal of Statistical Software, 2 (7), 1-23. doi: 10.18637/jss.v002.i07
Basford, K. E., McLachlan, G. J. and York, M. G. (1997). Modelling the distribution of stamp paper thickness via finite normal mixtures: The 1872 Hidalgo stamp issue of Mexico revisited. Journal of Applied Statistics, 24 (2), 169-180. doi: 10.1080/02664769723783
On the EM algorithm for overdispersed count data
McLachlan, G. J. (1997). On the EM algorithm for overdispersed count data. Statistical Methods in Medical Research, 6 (1), 76-98. doi: 10.1177/096228029700600106
An analysis of valve re-replacement after aortic valve replacement with biologic devices
McGiffin, DC, Galbraith, AJ, OBrien, MF, McLachlan, GJ, Naftel, DC, Adams, P, Reddy, S and Early, L (1997). An analysis of valve re-replacement after aortic valve replacement with biologic devices. Journal of Thoracic And Cardiovascular Surgery, 113 (2), 311-318. doi: 10.1016/S0022-5223(97)70328-3
High-breakdown linear discriminant analysis
Hawkins, DM and McLachlan, GJ (1997). High-breakdown linear discriminant analysis. Journal of The American Statistical Association, 92 (437), 136-143. doi: 10.2307/2291457
On Bayesian analysis of mixtures with an unknown number of components - Discussion
McLachlan, G (1997). On Bayesian analysis of mixtures with an unknown number of components - Discussion. Journal of The Royal Statistical Society Series B-methodological, 59 (4), 758-792.
Standard errors of fitted component means of normal mixtures
Basford, K. E., Greenway, D. R., McLachlan, G. J. and Peel, D. (1997). Standard errors of fitted component means of normal mixtures. Computational Statistics, 12 (1), 1-17.
The impact of the EM algorithm on medical statistics
McLachlan, G. (1997). The impact of the EM algorithm on medical statistics. Statistical Methods in Medical Research, 6 (1), 1-2. doi: 10.1191/096228097668772579
Maximum likelihood clustering via normal mixture models
McLachlan, GJ, Peel, D and Whiten, WJ (1996). Maximum likelihood clustering via normal mixture models. Signal Processing-Image Communication, 8 (2), 105-111. doi: 10.1016/0923-5965(95)00039-9
Likelihood-based approaches to pattern recognition
McLachlan, G. J. (1996). Likelihood-based approaches to pattern recognition. Far East Journal of Mathematical Sciences, 4 (Pt. 1), 1-29.
McLachlan, GJ, McLaren, CE and Matthews, D (1995). An Algorithm for the Likelihood Ratio Test of One Versus 2 Components in a Normal Mixture Model Fitted to Grouped and Truncated Data. Communications in Statistics-Simulation and Computation, 24 (4), 965-985. doi: 10.1080/03610919508813288
McLachlan, GJ and Scot, D (1995). Asymptotic Relative Efficiency of the Linear Discriminant Function Under Partial Nonrandom Classification of the Training Data. Journal of Statistical Computation and Simulation, 52 (4), 415-426. doi: 10.1080/00949659508811689
Relationship of Platelet-Aggregation to Bleeding After Cardiopulmonary Bypass
Ray, MJ, Hawson, Gat, Just, Sje, McLachlan, G and Obrien, M (1994). Relationship of Platelet-Aggregation to Bleeding After Cardiopulmonary Bypass. Annals of Thoracic Surgery, 57 (4), 981-986.
Parametric-Estimation in a Genetic Mixture Model with Application to Nuclear Family Data
Shoukri, MM and McLachlan, GJ (1994). Parametric-Estimation in a Genetic Mixture Model with Application to Nuclear Family Data. Biometrics, 50 (1), 128-139. doi: 10.2307/2533203
Lawoko, Cro and McLachlan, GJ (1994). Estimation of Mixing Proportions in the Presence of Autoregressively Correlated Training Data - the Case of 2 Univariate Normal-Populations. Communications in Statistics-Simulation and Computation, 23 (3), 591-613. doi: 10.1080/03610919408813189
Neural Networks and Related Methods for Classification - Discussion
Whittle, P, Kay, J, Hand, DJ, Tarassenko, L, Brown, PJ, Titterington, DM, Taylor, C, Gilks, WR, Critchley, F, Mayne, AJ, Wahba, G, Luttrell, SP, Baczkowski, AJ, Mardia, KV, Breiman, L, Buntine, W, Chatfield, C, Deveaux, RD, Darken, CJ, Ungar, LH, Glendinning, RH, Hastie, T, Tibshirani, R, McLachlan, GJ, Michie, D, Owen, AB, Wolpert, DH and Ripley, BD (1994). Neural Networks and Related Methods for Classification - Discussion. Journal of the Royal Statistical Society Series B-Methodological, 56 (3), 437-456.
On the role of finite mixture models in survival analysis.
McLachlan G.J. and McGiffin D.C. (1994). On the role of finite mixture models in survival analysis.. Statistical methods in medical research, 3 (3), 211-226. doi: 10.1177/096228029400300302
Relationship of platelet aggregation to bleeding after cardiopulmonary bypass
Ray, Michael J., Hawson, Geoffrey A.T., Just, Sarah J.E., McLachlan, Geoffery and O'Brien, Mark (1994). Relationship of platelet aggregation to bleeding after cardiopulmonary bypass. The Annals of Thoracic Surgery, 57 (4), 981-986. doi: 10.1016/0003-4975(94)90218-6
McGiffin, DC, Obrien, MF, Galbraith, AJ, McLachlan, GJ, Stafford, EG, Gardner, Mah, Pohlner, PG, Early, L and Kear, L (1993). An Analysis of Risk-Factors for Death and Mode-Specific Death After Aortic-Valve Replacement with Allograft, Xenograft, and Mechanical Valves. Journal of Thoracic and Cardiovascular Surgery, 106 (5), 895-911. doi: 10.1016/s0022-5223(19)34046-2
McLachlan, G (1993). A Connection Between the Logit Model, Normal Discriminant-Analysis, and Multivariate Normal Mixtures - Comment. American Statistician, 47 (1), 88-88.
McGiffin, DC, Galbraith, AJ, McLachlan, GJ, Stower, RE, Wong, ML, Stafford, EG, Gardner, Mah, Pohlner, PG and Obrien, MF (1992). Aortic-Valve Infection - Risk-Factors for Death and Recurrent Endocarditis After Aortic-Valve Replacement. Journal of Thoracic and Cardiovascular Surgery, 104 (2), 511-520. doi: 10.1016/s0022-5223(19)34813-5
Fitting finite mixture models in a regression context
Jones, P. N. and McLachlan, G. J. (1992). Fitting finite mixture models in a regression context. Australian Journal of Statistics, 34 (2), 233-240. doi: 10.1111/j.1467-842X.1992.tb01356.x
Jones, Peter N. and McLachlan, Geoffrey J. (1992). Improving the convergence rate of the EM algorithm for a mixture model fitted to grouped truncated data. Journal of Statistical Computation and Simulation, 43 (1-2), 31-44. doi: 10.1080/00949659208811426
Cluster analysis and related techniques in medical research
Mclachlan G.J. (1992). Cluster analysis and related techniques in medical research. Statistical Methods in Medical Research, 1 (1), 27-48. doi: 10.1177/096228029200100103
Fitting Mixture Distributions to Phenylthiocarbamide (ptc) Sensitivity
Jones, PN and McLachlan, GJ (1991). Fitting Mixture Distributions to Phenylthiocarbamide (ptc) Sensitivity. American Journal of Human Genetics, 48 (1), 117-120.
The analysis of time-related events after cardiac surgery
McGiffen, David C. and McLachlan, Geoffrey J. (1991). The analysis of time-related events after cardiac surgery. The AustralAsian Journal of Cardiac and Thoracic Surgery, 1 (1), 11-13. doi: 10.1016/1037-2091(91)90007-Y
Jones, P. N. and McLachlan, G. J. (1990). Algorithm AS 254: maximum likelihood estimation from grouped and truncated data with finite normal mixture models. Applied Statistics - Journal of the Royal Statistical Society Series C, 39 (2), 273-282. doi: 10.2307/2347776
Laplace-normal mixtures fitted to wind shear data
Jones, P. N. and McLachlan, G. J. (1990). Laplace-normal mixtures fitted to wind shear data. Journal of Applied Statistics, 17 (2), 271-276. doi: 10.1080/757582839
Mixture-Models for Partially Unclassified Data - a Case-Study of Renal Venous Renin in Hypertension
McLachlan, GJ and Gordon, RD (1989). Mixture-Models for Partially Unclassified Data - a Case-Study of Renal Venous Renin in Hypertension. Statistics in Medicine, 8 (10), 1291-1300. doi: 10.1002/sim.4780081012
Bias Associated with the Discriminant-Analysis Approach to the Estimation of Mixing Proportions
Lawoko, Cro and McLachlan, GJ (1989). Bias Associated with the Discriminant-Analysis Approach to the Estimation of Mixing Proportions. Pattern Recognition, 22 (6), 763-766. doi: 10.1016/0031-3203(89)90012-5
Modeling Mass-Size Particle Data by Finite Mixtures
Jones, PN and McLachlan, GJ (1989). Modeling Mass-Size Particle Data by Finite Mixtures. Communications in Statistics-Theory and Methods, 18 (7), 2629-2646. doi: 10.1080/03610928908830054
Fitting Mixture-Models to Grouped and Truncated Data Via the Em Algorithm
McLachlan, GJ and Jones, PN (1988). Fitting Mixture-Models to Grouped and Truncated Data Via the Em Algorithm. Biometrics, 44 (2), 571-578. doi: 10.2307/2531869
Further Results On Discrimination with Auto-Correlated Observations
Lawoko, Cro and McLachlan, GJ (1988). Further Results On Discrimination with Auto-Correlated Observations. Pattern Recognition, 21 (1), 69-72. doi: 10.1016/0031-3203(88)90073-8
On the Choice of Starting Values for the Em Algorithm in Fitting Mixture-Models
McLachlan, GJ (1988). On the Choice of Starting Values for the Em Algorithm in Fitting Mixture-Models. Statistician, 37 (4-5), 417-425. doi: 10.2307/2348768
A Note On the Aitkin-Rubin Approach to Hypothesis-Testing in Mixture-Models
Quinn, BG, McLachlan, GJ and Hjort, NL (1987). A Note On the Aitkin-Rubin Approach to Hypothesis-Testing in Mixture-Models. Journal of the Royal Statistical Society Series B-Methodological, 49 (3), 311-314. doi: 10.1111/j.2517-6161.1987.tb01700.x
McLachlan, GJ (1987). On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture. Applied Statistics-Journal of the Royal Statistical Society Series C, 36 (3), 318-324. doi: 10.2307/2347790
Assessing the Performance of An Allocation Rule
McLachlan, GJ (1986). Assessing the Performance of An Allocation Rule. Computers & Mathematics with Applications-Part a, 12 (2), 261-272. doi: 10.1016/0898-1221(86)90079-9
Lawoko, Cro and McLachlan, GJ (1986). Asymptotic Error Rates of the W-Statistics and Z-Statistics When the Training Observations Are Dependent. Pattern Recognition, 19 (6), 467-471. doi: 10.1016/0031-3203(86)90045-2
Cluster-Analysis in a Randomized Complete Block Design
Basford, KE and McLachlan, GJ (1985). Cluster-Analysis in a Randomized Complete Block Design. Communications in Statistics-Theory and Methods, 14 (2), 451-463. doi: 10.1080/03610928508828924
Discrimination with Auto-Correlated Observations
Lawoko, Cro and McLachlan, GJ (1985). Discrimination with Auto-Correlated Observations. Pattern Recognition, 18 (2), 145-149. doi: 10.1016/0031-3203(85)90038-X
Estimation of allocation rates in a cluster-analysis context
Basford, K. E. and McLachlan, G. J. (1985). Estimation of allocation rates in a cluster-analysis context. Journal of the American Statistical Association, 80 (390), 286-293. doi: 10.2307/2287884
Likelihood Estimation with Normal Mixture-Models
Basford, KE and McLachlan, GJ (1985). Likelihood Estimation with Normal Mixture-Models. Applied Statistics-Journal of the Royal Statistical Society Series C, 34 (3), 282-289. doi: 10.2307/2347474
The Mixture Method of Clustering Applied to 3-Way Data
Basford, KE and McLachlan, GJ (1985). The Mixture Method of Clustering Applied to 3-Way Data. Journal of Classification, 2 (1), 109-125. doi: 10.1007/BF01908066
Estimation of Mixing Proportions - a Case-Study
Do, K and McLachlan, GJ (1984). Estimation of Mixing Proportions - a Case-Study. Applied Statistics-Journal of the Royal Statistical Society Series C, 33 (2), 134-140. doi: 10.2307/2347437
Lawoko, Cro and McLachlan, GJ (1983). Some Asymptotic Results On the Effect of Auto-Correlation On the Error Rates of the Sample Linear Discriminant Function. Pattern Recognition, 16 (1), 119-121. doi: 10.1016/0031-3203(83)90014-6
On the Bias and Variance of Some Proportion Estimators
McLachlan, GJ (1982). On the Bias and Variance of Some Proportion Estimators. Communications in Statistics Part B-Simulation and Computation, 11 (6), 715-726. doi: 10.1080/03610918208812290
On the Likelihood Ratio Test for Compound Distributions for Homogeneity of Mixing Proportions
McLachlan, GJ, Lawoko, Cro and Ganesalingam, S (1982). On the Likelihood Ratio Test for Compound Distributions for Homogeneity of Mixing Proportions. Technometrics, 24 (4), 331-334. doi: 10.2307/1267829
On the likelihood ratio test for compound distributions for homogeneity of mixing proportions
McLachlan, G. J., Lawoko, C. R O and Ganesalingam, S. (1982). On the likelihood ratio test for compound distributions for homogeneity of mixing proportions. Technometrics, 24 (4), 331-334. doi: 10.1080/00401706.1982.10487796
Updating a Discriminant Function On the Basis of Unclassified Data
McLachlan, GJ and Ganesalingam, S (1982). Updating a Discriminant Function On the Basis of Unclassified Data. Communications in Statistics Part B-Simulation and Computation, 11 (6), 753-767. doi: 10.1080/03610918208812293
Mathematics and Statistics for the Bio-Sciences - Eason,g, Coles,cw, Gettinby,g
McLachlan, GJ (1981). Mathematics and Statistics for the Bio-Sciences - Eason,g, Coles,cw, Gettinby,g. Biometrics, 37 (2), 417-417. doi: 10.2307/2530436
Ganesalingam, S and McLachlan, GJ (1981). Some Efficiency Results for the Estimation of the Mixing Proportion in a Mixture of 2 Normal-Distributions. Biometrics, 37 (1), 23-33. doi: 10.2307/2530519
A Comparison of the Mixture and Classification Approaches to Cluster-Analysis
Ganesalingam, S and McLachlan, GJ (1980). A Comparison of the Mixture and Classification Approaches to Cluster-Analysis. Communications in Statistics Part A-Theory and Methods, 9 (9), 923-933. doi: 10.1080/03610928008827932
A Note On Bias Correction in Maximum Likelihood Estimation with Logistic Discrimination
McLachlan, GJ (1980). A Note On Bias Correction in Maximum Likelihood Estimation with Logistic Discrimination. Technometrics, 22 (4), 621-627. doi: 10.2307/1268202
Error Rate Estimation On the Basis of Posterior Probabilities
Ganesalingam, S and McLachlan, GJ (1980). Error Rate Estimation On the Basis of Posterior Probabilities. Pattern Recognition, 12 (6), 405-413. doi: 10.1016/0031-3203(80)90016-3
Logicstic regression compared to normal discrimination for non-normal populations
Byth, K. and McLachlan, G. J. (1980). Logicstic regression compared to normal discrimination for non-normal populations. Australian Journal of Statistics, 22 (2), 188-196. doi: 10.1111/j.1467-842X.1980.tb01166.x
McLachlan, GJ (1980). On the Relationship Between the F-Test and the Overall Error Rate for Variable Selection in 2-Group Discriminant-Analysis. Biometrics, 36 (3), 501-510. doi: 10.2307/2530218
On the mean square error associated with adaptive generalized ridge regression
McLachlan G.J. (1980). On the mean square error associated with adaptive generalized ridge regression. Biometrical Journal, 22 (2), 125-129. doi: 10.1002/bimj.4710220205
Selection of Variables in Discriminant-Analysis
McLachlan, G (1980). Selection of Variables in Discriminant-Analysis. Biometrics, 36 (3), 554-554.
THE COVARIANCE ANALYSIS OF SOME CENSORED SURVIVAL DATA FROM A LARGE SCALE STUDY OF MELANOMA
McLachlan, G. J. and Holt, J. N. (1980). THE COVARIANCE ANALYSIS OF SOME CENSORED SURVIVAL DATA FROM A LARGE SCALE STUDY OF MELANOMA. Australian Journal of Statistics, 22 (3), 237-249. doi: 10.1111/j.1467-842X.1980.tb01173.x
McLachlan, GJ (1980). The Efficiency of Efrons Bootstrap Approach Applied to Error Rate Estimation in Discriminant-Analysis. Journal of Statistical Computation and Simulation, 11 (3-4), 273-279. doi: 10.1080/00949658008810414
A case study of two clustering methods based on maximum likelihood
Ganesalingam, S. and McLachlan, G. J. (1979). A case study of two clustering methods based on maximum likelihood. Statistica Neerlandica, 33 (2), 81-90. doi: 10.1111/j.1467-9574.1979.tb00665.x
Comparison of the Estimative and Predictive Methods of Estimating Posterior Probabilities
McLachlan, G. J. (1979). Comparison of the Estimative and Predictive Methods of Estimating Posterior Probabilities. Communications in Statistics Part A-Theory and Methods, 8 (9), 919-929. doi: 10.1080/03610927908827807
Expected error rates for logistic regression versus normal discriminant analysis
McLachlan, G. J. and Byth, K. (1979). Expected error rates for logistic regression versus normal discriminant analysis. Biometrical Journal, 21 (1), 47-56. doi: 10.1002/bimj.4710210107
Ganesalingam, S. and McLachlan, G. J. (1979). Small sample results for a linear discriminant function estimated from a mixture of normal populations. Journal of Statistical Computation and Simulation, 9 (2), 151-158. doi: 10.1080/00949657908810306
Byth, K and McLachlan, GJ (1978). Biases Associated with Maximum Likelihood Methods of Estimation of Multivariate Logistic Risk Function. Communications in Statistics Part A-Theory and Methods, 7 (9), 877-890. doi: 10.1080/03610927808827679
Efficiency of a Linear Discriminant Function Based On Unclassified Initial Samples
Ganesalingam, S and McLachlan, GJ (1978). Efficiency of a Linear Discriminant Function Based On Unclassified Initial Samples. Biometrika, 65 (3), 658-662. doi: 10.1093/biomet/65.3.658
Small sample results for partial classification with the Studentized statistic W
McLachlan, G. J. (1978). Small sample results for partial classification with the Studentized statistic W. Biometrical Journal, 20 (7-8), 639-644. doi: 10.1002/bimj.197800003
Constrained Sample Discrimination with Studentized Classification Statistic-W
McLachlan, GJ (1977). Constrained Sample Discrimination with Studentized Classification Statistic-W. Communications in Statistics Part A-Theory and Methods, 6 (6), 575-583. doi: 10.1080/03610927708827515
McLachlan, GJ (1977). Estimating Linear Discriminant Function From Initial Samples Containing a Small Number of Unclassified Observations. Journal of the American Statistical Association, 72 (358), 403-406. doi: 10.2307/2286807
McLachlan, GJ (1977). Note On Choice of a Weighting Function to Give An Efficient Method for Estimating Probability of Misclassification. Pattern Recognition, 9 (3), 147-149. doi: 10.1016/0031-3203(77)90012-7
The bias of sample based posterior probabilities
McLachlan, G. J. (1977). The bias of sample based posterior probabilities. Biometrical Journal, 19 (6), 421-426. doi: 10.1002/bimj.4710190604
Bias of Apparent Error Rate in Discriminant-Analysis
McLachlan, GJ (1976). Bias of Apparent Error Rate in Discriminant-Analysis. Biometrika, 63 (2), 239-244. doi: 10.2307/2335615
Criterion for Selecting Variables for Linear Discriminant Function
McLachlan, GJ (1976). Criterion for Selecting Variables for Linear Discriminant Function. Biometrics, 32 (3), 529-534. doi: 10.2307/2529742
Further Results On Effect of Intraclass Correlation Among Training Samples in Discriminant-Analysis
McLachlan, GJ (1976). Further Results On Effect of Intraclass Correlation Among Training Samples in Discriminant-Analysis. Pattern Recognition, 8 (4), 273-275. doi: 10.1016/0031-3203(76)90047-9
Confidence Intervals for Conditional Probability of Misallocation in Discriminant-Analysis
McLachlan, GJ (1975). Confidence Intervals for Conditional Probability of Misallocation in Discriminant-Analysis. Biometrics, 31 (1), 161-167. doi: 10.2307/2529717
McLachlan, GJ (1975). Iterative Reclassification Procedure for Constructing An Asymptotically Optimal Rule of Allocation in Discriminant-Analysis. Journal of the American Statistical Association, 70 (350), 365-369. doi: 10.2307/2285824
McLachlan, G. J. (1975). Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in discriminant analysis. Journal of the American Statistical Association, 70 (350), 365-369. doi: 10.1080/01621459.1975.10479874
Some Expected Values for Error Rates of Sample Quadratic Discriminant Function
McLachlan, GJ (1975). Some Expected Values for Error Rates of Sample Quadratic Discriminant Function. Australian Journal of Statistics, 17 (3), 161-165. doi: 10.1111/j.1467-842x.1975.tb00953.x
Asymptotic Distributions of Conditional Error Rate and Risk in Discriminant-Analysis
McLachla.GJ (1974). Asymptotic Distributions of Conditional Error Rate and Risk in Discriminant-Analysis. Biometrika, 61 (1), 131-135. doi: 10.1093/biomet/61.1.131
Asymptotic Unbiased Technique for Estimating Error Rates in Discriminant-Analysis
McLachla.GJ (1974). Asymptotic Unbiased Technique for Estimating Error Rates in Discriminant-Analysis. Biometrics, 30 (2), 239-249. doi: 10.2307/2529646
Estimation of Errors of Misclassification On Criterion of Asymptotic Mean-Square Error
McLachla.GJ (1974). Estimation of Errors of Misclassification On Criterion of Asymptotic Mean-Square Error. Technometrics, 16 (2), 255-260. doi: 10.2307/1267948
McLachla.GJ (1974). Relationship in Terms of Asymptotic Mean-Square Error Between Separate Problems of Estimating Each of 3 Types of Error Rate of Linear Discriminant Function. Technometrics, 16 (4), 569-575. doi: 10.2307/1267609
McLachlan, G. J. (1974). The relationship in terms of asymptotic mean square error between the separate problems of estimating each of the three types of error rate of the linear discriminant function. Technometrics, 16 (4), 569-575. doi: 10.1080/00401706.1974.10489239
The errors of allocation and their estimators in the two-population discrimination problem
McLachlan, Geoffrey J. (1973). The errors of allocation and their estimators in the two-population discrimination problem. Bulletin of the Australian Mathematical Society, 9 (01), 149-150. doi: 10.1017/s000497270004301x
Asymptotic Expansion of Expectation of Estimated Error Rate in Discriminant-Analysis
McLachla.GJ (1973). Asymptotic Expansion of Expectation of Estimated Error Rate in Discriminant-Analysis. Australian Journal of Statistics, 15 (3), 210-214. doi: 10.1111/j.1467-842X.1973.tb00201.x
Asymptotic Expansion for Variance of Errors of Misclassification of Linear Discriminant Function
McLachla.GJ (1972). Asymptotic Expansion for Variance of Errors of Misclassification of Linear Discriminant Function. Australian Journal of Statistics, 14 (1), 68-72. doi: 10.1111/j.1467-842X.1972.tb00339.x
Asymptotic Results for Discriminant Analysis When Initial Samples Are Misclassified
McLachla.GJ (1972). Asymptotic Results for Discriminant Analysis When Initial Samples Are Misclassified. Technometrics, 14 (2), 415-&. doi: 10.2307/1267432
Exploratory data analysis of TCGA skin cutaneous melanoma RNA-seq data
Zhang, Min, Arief, Vivi, McLachlan, Geoffrey, Nguyen, Quan and Basford, Kaye (2022). Exploratory data analysis of TCGA skin cutaneous melanoma RNA-seq data. Australasian Applied Statistics Conference (AASC), Inverloch, VIC Australia, 28 November - 2 December 2022.
AEGC Machine Learning Workshop presentation
Chatterjee, Robindra, Valenta, Richard, McLachlan, Geoffrey and Weatherley, Dion (2021). AEGC Machine Learning Workshop presentation. Australian Exploration Geoscience Conference, Online, 14-17 September 2021.
Chatterjee, Robindra , Valenta, Richard , McLachlan, Geoffrey and Weatherley, Dion (2021). Extending FaultSeg3D to Minerals Seismic: Part 1 – A synthetic 3D-seismic training-volume generator for preparing data replicating a hardrock terrane to train an automatic-fault-prediction algorithm. Australian Earth Science Convention, Virtual, 9-12 February 2021.
On Mean And/or Variance Mixtures of Normal Distributions
Lee, Sharon X. and McLachlan, Geoffrey J. (2021). On Mean And/or Variance Mixtures of Normal Distributions. 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), Cassino, Italy, 11–13 September 2019. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-69944-4_13
Lee, Sharon X. and McLachlan, Geoffrey J. (2020). Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk. 20th International Congress on Modelling and Simulation - Adapting to Change: The Multiple Roles of Modelling, MODSIM 2013 , Adelaide, SA, Australia, 1 - 6 December 2013. Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ).
PPEM: privacy-preserving EM learning for mixture models
Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2019). PPEM: privacy-preserving EM learning for mixture models. 8th International Conference on Applications and Techniques in Information Security, ATIS 2017, Auckland, New Zealand, 6-7 July 2017. Oxford, United Kingdom: John Wiley & Sons. doi: 10.1002/cpe.5208
Flexible modelling via multivariate skew distributions
McLachlan, Geoffrey J. and Lee, Sharon X. (2019). Flexible modelling via multivariate skew distributions. Research School on Statistics and Data Science (RSSDS 2019), Melbourne, VIC, Australia, 24–26 July 2019. Singapore, Singapore: Springer Singapore. doi: 10.1007/978-981-15-1960-4_4
Positive data kernel density estimation via the LogKDE package for R
Jones, Andrew T., Nguyen, Hien D. and McLachlan, Geoffrey J. (2019). Positive data kernel density estimation via the LogKDE package for R. AusDM 2018: 16th Australasian Conference on Data Mining, Bahrurst, NSW, Australia, 28 - 30 November 2018. Singapore, Singapore: Springer Singapore. doi: 10.1007/978-981-13-6661-1_21
Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering
Leemaqz, Kaleb L., Lee, Sharon X. and McLachlan, Geoffrey J. (2017). Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering. 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, Australia, 1 - 4 August 2017. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/Trustcom/BigDataSE/ICESS.2017.356
Nguyen, Hien D. and McLachlan, Geoffrey J. (2017). Iteratively-reweighted least-squares fitting of support vector machines: a majorization–minimization algorithm approach. Future Technologies Conference (FTC) 2017, Vancouver, Canada, 29-30 November 2017. Piscataway, NJ United States: IEEE.
Ng, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. 15th Conference of the International Federation of Classification Societies (IFCS), Bologna, Italy, July 5-8, 2015. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-55723-6_4
Privacy distributed three-party learning of Gaussian mixture models
Leemaqz, Kaleb L., Lee, Sharon X. and McLachlan, Geoffrey J. (2017). Privacy distributed three-party learning of Gaussian mixture models. International Conference on Applications and Technologies in Information Security (ATIS), Auckland, New Zealand, 6-7 July 2017. Singapore: Springer Singapore. doi: 10.1007/978-981-10-5421-1_7
A simple parallel EM algorithm for statistical learning via mixture models
Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2016). A simple parallel EM algorithm for statistical learning via mixture models. International Conference on Digital Image Computing, Gold Coast, QLD, Australia, 30 November - 2 December,2016. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/DICTA.2016.7796997
Finding group structures in "Big Data" in healthcare research using mixture models
Ng, Shu-Kay and McLachlan, Geoffrey J. (2016). Finding group structures in "Big Data" in healthcare research using mixture models. IEEE International Conference on Bioinformatics and Biomedicine, Shenzhen, China, 15-18 December 2016. Piscataway, NJ, United States: IEE Computer Society. doi: 10.1109/BIBM.2016.7822692
On mixture modelling with multivariate skew distributions
Lee, Sharon X. and McLachlan, Geoffrey J. (2016). On mixture modelling with multivariate skew distributions. COMPSTAT: International Conference on Computational Statistics, Oviedo, Spain, 23-26 August 2016. The Hague, The Netherlands: The International Statistical Institute/International Association for Statistical Computing.
Robust estimation of mixtures of skew-normal distributions
García-Escudero, L. A., Greselin, F., Mayo-Iscar, A. and McLachlan, G. J. (2016). Robust estimation of mixtures of skew-normal distributions. Scientific Meeting of the Italian Statistical Society, Salerno, Italy, 8-10 November 2016. Fisciano, Italy: Dipartimento di Scienze Economiche e Statistiche, University of Salerno..
Unsupervised component-wise EM learning for finite mixtures of skew t-distributions
Lee, Sharon X. and McLachlan, Geoffrey J. (2016). Unsupervised component-wise EM learning for finite mixtures of skew t-distributions. 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, 12-15 December 2016. New York, NY, United States: Springer. doi: 10.1007/978-3-319-49586-6_49
Application of multiple imputation to incomplete three-way three-mode multi-environment trial data
Tian, T., McLachlan, G., Dieters, M. and Basford, K. (2014). Application of multiple imputation to incomplete three-way three-mode multi-environment trial data. International Biometric Conference, Florence (Italy), 6-11 July 2014. Florence, Italy: International Biometric Society.
Asymptotic inference for hidden process regression models
Nguyen, Hien D. and McLachlan, Geoffrey J. (2014). Asymptotic inference for hidden process regression models. 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, Australia, 29 June - 2 July 2014. Piscataway, NJ, United States: IEEE. doi: 10.1109/SSP.2014.6884624
Making sense of a random world through statistics
McLachlan, Geoff (2014). Making sense of a random world through statistics. AusDM 2014, Brisbane, QLD, Australia, 27-28 November 2014. Darlinghurst, NSW, Australia: Australian Computer Society.
Mixture of regression models with latent variables and sparse coefficient parameters
Ng, Shu-Kay and McLachlan, Geoffrey J. (2014). Mixture of regression models with latent variables and sparse coefficient parameters. COMPSTAT 2014, Geneva Switzerland, 19- 22 August 2014. Hague, Netherlands: The International Statistical Institute/International Association for Statistical Computing.
A common factor-analytic model for classification
Sun, Mingzhu and McLachlan, Geoffrey J (2013). A common factor-analytic model for classification. IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai China, 18 - 21 December 2013. Piscataway, NJ United States: I E E E. doi: 10.1109/BIBM.2013.6732722
Tian, Ting, McLachlan, Geoff, Dieters, Mark and Basford, Kaye (2013). Evaluating methods of estimating missing values for three-way three-mode multi-environment trial data. Biometrics by the Canals: The International Biometric Society Australasian Region Conference 2013, Mandura, WA, Australia, 1-5 December, 2013.
Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk. International Congress on Modelling and Simulation, Adelaide, SA, Australia, 1/12/2013/6/12/2013. Melbourne, Australia: Modelling and Simulation Society of Australia and New Zealand.
On finite mixtures of skew distributions
McLachlan, Geoffrey J. and Leemaqz, Sharon X. (2013). On finite mixtures of skew distributions. 28th International Workshop on Statistical Modelling, Palermo, Italy, 8-12 July 2013. Amsterdam: Statistical Modelling Society.
Kim, Sunghoon, Li, Guo-Zheng, Ressom, Habtom, Hughes, Michael, Liu, Baoyan, McLachlan, Geoff, Liebman, Michael, Sun, Hongye and Hu, Xiaohua (2013). Preface. 2013 IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China, 18-21 December 2013. Minerals, Metals and Materials Society. doi: 10.1109/BIBM.2013.6732445
Spatial false discovery rate control for magnetic resonance imaging studies
Nguyen, Hien D., McLachlan, Geoffrey J., Janke, Andrew L., Cherbuin, Nicolas, Sachdev, Perminder and Anstey, Kaarin J. (2013). Spatial false discovery rate control for magnetic resonance imaging studies. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013, Hobart, TAS, 26 - 28 November 2013. Piscataway, NJ United States: I E E E. doi: 10.1109/DICTA.2013.6691531
Ng, Shu-Kay and McLachlan, Geoffrey J. (2013). Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes. IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai, China, 18 - 21 December 2013. Piscataway, NJ United States: I E E E. doi: 10.1109/BIBM.2013.6732501
Nikulin, Vladimir, Huang, Tian-Hsiang and McLachlan, Geoffrey J. (2010). A comparative study of two matrix factorization methods applied to the classification of gene expression rate. IEEE International Conference on Bioinformatics & Biomedicine, Hong Kong, 18-21 December 2010. Los Alamitos, CA, U.S.A.: IEEE Computer Society. doi: 10.1109/bibm.2010.5706640
Automated high-dimensional flow cytometric data analysis
Pyne, Saumyadipta, Hu, Xinli, Wang, Kui, Rossin, Elizabeth, Lin, Tsung-I, Maier, Lisa, Baecher-Allan, Clare, McLachlan, Geoffrey, Tamayo, Pablo, Hafler, David, De Jager, Philip and Mesirov, Jill (2010). Automated high-dimensional flow cytometric data analysis. 14th Annual International Conference on Research in Computational Molecular Biology, Lisbon, Portugal, 25-28 April 2010. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-12683-3_41
Clustering of High-Dimensional Data via Finite Mixture Models
McLachlan, Geoff J. and Baek, Jangsun (2010). Clustering of High-Dimensional Data via Finite Mixture Models. 32nd Annual Conference of the German-Classification-Society, Hamburg Germany, Jul 16-18, 2008. BERLIN: SPRINGER-VERLAG BERLIN. doi: 10.1007/978-3-642-01044-6_3
Identifying fibre bundles with regularized k-means clustering applied to grid-based data
Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). Identifying fibre bundles with regularized k-means clustering applied to grid-based data. 2010 International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, 18-23 July 2010. United States: IEEE Computer Society. doi: 10.1109/IJCNN.2010.5596562
On relations between genes and metagenes obtained via gradient-based matrix factorization
Huang, Tian-Hsiang, Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). On relations between genes and metagenes obtained via gradient-based matrix factorization. 2010 IEEE/ICME International Conference on Complex Medical Engineering, Gold Coast, Australia, 13-15 July 2010. Piscataway, United States: IEEE Computer Society. doi: 10.1109/ICCME.2010.5558880
On the gradient-based algorithm for matrix factorization applied to dimensionality reduction
Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). On the gradient-based algorithm for matrix factorization applied to dimensionality reduction. BIOINFORMATICS 2010: 1st International Conference on Bioinformatics, Valencia, Spain, 20-23 January 2010. Portugal: Institute for Systems and Technologies of Information, Control and Communication.
Penalized principal component analysis of microarray data
Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). Penalized principal component analysis of microarray data. 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2009, Genoa, Italy, 15-17 October, 2009. Germany: Springer. doi: 10.1007/978-3-642-14571-1_7
RSCTC 2010 Discovery Challenge: Mining DNA microarray data for medical diagnosis and treatment
Wojnarski, Marcin, Janusz, Andrzej, Nyugen, Hung Son, Bazan, Jan, Luo, ChuanJiang, Chen, Ze, Hu, Feng, Wang, Guoyin, Guan, Lihe, Luo, Huan, Gao, Juan, Shen, Yuanxia, Nikulin, Vladimir, Huang, Tian-Hsiang, McLachlan, Geoffrey J., Bosnjak, Matko and Gamberger, Dragan (2010). RSCTC 2010 Discovery Challenge: Mining DNA microarray data for medical diagnosis and treatment. 7th International Conference on Rough Sets and Current Trends in Computing (RSCTC 2010), Warsaw, Poland, 28-30 June 2010. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-13529-3_3
Use of mixture models in multiple hypothesis testing with applications in bioinformatics
McLachlan, Geoffrey J. and Wockner, Leesa (2010). Use of mixture models in multiple hypothesis testing with applications in bioinformatics. Classification as a Tool for Research (GfKl 2009), Dresden, Germany, 13-18 March 2009. doi: 10.1007/978-3-642-10745-0-18
Classification of imbalanced marketing data with balanced random sets
Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Classification of imbalanced marketing data with balanced random sets. AISTATS 2009, Clearwater Beach, FL, United States, 16-18 April 2009. Cambridge, MA, United States: M I T Press.
Ensemble approach for the classification of imbalanced data
Nikulin, Vladimir, McLachlan, Geoffrey J. and Ng, Shu Kay (2009). Ensemble approach for the classification of imbalanced data. AI 2009: Advances in Artificial Intelligence, Melbourne, VIC, Australia, 1-4 December 2009. Berlin, Germany: Springer. doi: 10.1007/978-3-642-10439-8_30
Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data
Wang, Kui, Ng, Shu-Kay and McLachlan, Geoffrey J. (2009). Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data. 2009 Conference of Digital Image Computing: Techniques and Applications, Melbourne, Australia, 1-3 December 2009. Los Alamitos, California: IEEE Computer Society. doi: 10.1109/DICTA.2009.88
On a general method for matrix factorisation applied to supervised classification
Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). On a general method for matrix factorisation applied to supervised classification. 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, Washington, D.C., U.S.A., 1-4 November 2009. Piscataway, NJ, United States: IEEE. doi: 10.1109/BIBMW.2009.5332135
Regularised k-means clustering for dimension reduction applied to supervised classification
Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Regularised k-means clustering for dimension reduction applied to supervised classification. Sixth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics 2009, Genova, Italy, 15-17 October 2009. Salerno, Italy: DMI Proceedings Series.
Clustering via mixture regression models with random effects
McLachlan, G. J., Ng, S. K. and Wang, K. (2008). Clustering via mixture regression models with random effects. 18th Symposium on Computational Statistics (COMSTAT 2008), Porto, Portugal, 24-29 August 2008. Heidelberg, Germany: Physica-Verlag,. doi: 10.1007/978-3-7908-2084-3_33
Merging algorithm to reduce dimensionality in application to web-mining
Nikulin, V and McLachlan, GJ (2007). Merging algorithm to reduce dimensionality in application to web-mining. 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Qld, Australia, 2-6 December, 2007. Berlin, Germany: Springer Berlin / Heidelberg. doi: 10.1007/978-3-540-76928-6_88
Lenzenweger, M. F., McLachlan, G. and Rubin, D. B. (2007). Resolving the latent structure of schizophrenia endophenotypes using em-based finite mixture modeling. 10th International Congress on Schizophrenia Research, Savannah Ga, 02-06 April 2005. Oxford, United Kingdom: Oxford University Press. doi: 10.1093/schbul/sbm004
McLaren, C. E., Gordeuk, V. R., Chen, W. P., Barton, J. C., Acton, R. T., Speechley, M., Castro, O., Adams, P. C., Snively, B. M., Harris, E. L., Reboussin, D. M., McLachlan, G. J., Bean, R. and McLaren, G. D. (2007). Subpopulations with iron deficiency, liver disease, or HFE mutations revealed by statistical mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African American, Hispanics, and Whites. 49th Annual Meeting of the American Society of Hematology, Atlanta, GA, U.S.A., 8 - 11 December 2007. Washington, DC, U.S.A.: American Society of Hematology.
A mixture model with random-effects components for clustering correlated gene-expression profiles
Ng, S. K., McLachlan, G. J., Wang, K., Jones, L. Ben-Tovim and Ng, S. W. (2006). A mixture model with random-effects components for clustering correlated gene-expression profiles. doi: 10.1093/bioinformatics/btl165
Ng, S K, McLachlan, G J, Bean, R W and NG, SW (2006). Clustering replicated microarray data in mixtures of random effects models for varius covariance structures. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB, Hobart, Australia, 4 December 2006. Sydney: The Australian Computer Society.
Issues of robustness and high dimensionality in cluster analysis
Basford, Kaye, McLachlan, Geoff and Bean, Richard (2006). Issues of robustness and high dimensionality in cluster analysis. 17th Symposium on Computational Statistics (COMSTAT 2006), Rome, Italy, 28 August - 1 September 2006. Rome, Italy: Physica-Verlag. doi: 10.1007/978-3-7908-1709-6_1
Multilevel modelling for inference of genetic regulatory networks
Ng, Shu-Kay, Wang, Kui and McLachlan, Geoffrey J. (2006). Multilevel modelling for inference of genetic regulatory networks. Complex Systems, Brisbane, Australia, 11-14 December 2005. Bellingham, WA, United States: SPIE - International Society for Optical Engineering. doi: 10.1117/12.638449
Application of mixture models to detect differentially expressed genes
Jones, LBT, Bean, R, McLachlan, G and Zhu, J (2005). Application of mixture models to detect differentially expressed genes. Berlin: Springer-Verlag Berlin. doi: 10.1007/11508069_55
Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data
Ng, A.S.K. and McLachlan, G. J. (2005). Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data. WDIC2005, Griffith University, 21 February 2005. Brisbane, Australia: Australian Pattern Recognition Society.
Normalized Gaussian Networks with Mixed Feature Data
Ng, A. S. K. and McLachlan, G. J. (2005). Normalized Gaussian Networks with Mixed Feature Data. 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, 5-9 Dec 2005. Berlin, Germany: Springer-Verlag. doi: 10.1007/11589990_101
Linking gene-expression experiments with survival-time data
Jones, L., Ng, A.S. K., Monico, K. A. and McLachlan, G. J. (2004). Linking gene-expression experiments with survival-time data. 19th International Workshop on Statistical Modelling, Florence, 4-8 July 2004. Italy: Firenze University Press.
McLachlan, G. J., Chang, S., Mar, J. and Ambroise, C. (2004). On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples. Second Asia-Pacific Bioinformatics Conference, Dunedin, New Zealand, 18-22 January 2004. Sydney, Australia: Australian Computer Society.
On clustering by mixture models
McLachlan, GJ, Ng, SK and Peel, D (2003). On clustering by mixture models. 25th Annual Conference of the German-Classification-Society, Munich Germany, Mar 14-16, 2001. BERLIN: SPRINGER-VERLAG BERLIN.
Robust estimation in Gaussian mixtures using multiresolution Kd -trees
Ng, A. S. K. and McLachlan, G. J. (2003). Robust estimation in Gaussian mixtures using multiresolution Kd -trees. Seventh International Conference on Digital Image Computing: Techniques and Applications, DICTA 2003, Sydney, Australia, 10-12 December 2003. Melbourne, Australia: CSIRO Publishing.
Segmentation of brain MR images with bias field correction
Kim, S-G., Ng, A.S. K., McLachlan, G. J. and Wang, D. (2003). Segmentation of brain MR images with bias field correction. WDIC 2003, The University of Queensland, Brisbane, 7 February 2003. Brisbane, Australia: The University of Queensland.
Ng, A.S. K. and McLachlan, G. J. (2002). On speeding up the EM algorithm in pattern recognition: A comparison of incremental and multiresolution KD -tree-based approaches. Proc. of the Sixth Digital Image Computing Techniques & Applications, Melbourne University, 21-22 January. Melbourne: Australian Pattern Recognition Society.
McLachlan, G. J. and Peel, D. (2000). Mixture of factor analyzers. Seventh International Conference on Machine Learning (ICML - 2000), California, United States, June 29 - July 2 2000. San Francisco, CA United States: Morgan Kaufmann.
Multivariate mixture models for classification of anemias
McLaren, C. E., Cadez, I. V., Smyth, P. and McLachlan, G. J. (2000). Multivariate mixture models for classification of anemias. 2000 Proceedings of the Biometrics Sect. of the Amer.Stat.Ass, Indianapolis, USA, August 2000. Virginia, USA: American Statistical Association.
Computing issues for the EM algorithm in mixture models
Mclachlan, G. J. and Peel, D. (1999). Computing issues for the EM algorithm in mixture models. Interface '99, Schaumbury, Illinois, June 1999. Fairfax Station, Virginia: Interface Foundation of North America.
Extending the two-way mixture model program EMMIX to analyse incomplete data
Greenway, D. R., Peel, D., Basford, K. E. and McLachlan, G. J. (1999). Extending the two-way mixture model program EMMIX to analyse incomplete data. Biometrics 99, Univ. of Tas., Hobart, Tas, Aust., 12-16 December 1999. Hobart, Aust.: Int. Biomtric Society, Australasian Region.
Hierarchical models for the screening of iron deficiency anemia
Cadez, I. V., McLaren, C. E., Smyth, P. and Mclachlan, G. J. (1999). Hierarchical models for the screening of iron deficiency anemia. Sixteenth International Conference on Machine Learning (ICML-99), Bled, Slovenia, June 27-30, 1999. Los Gatos, California: Morgan Kaufmann.
MIXFIT: An algorithm for the automatic fitting and testing of normal mixture models
McLachlan, GJ and Peel, D (1998). MIXFIT: An algorithm for the automatic fitting and testing of normal mixture models. 14th International Conference on Pattern Recognition, Brisbane Australia, Aug 16-20, 1998. LOS ALAMITOS: IEEE COMPUTER SOC. doi: 10.1109/icpr.1998.711203
Mining in the presence of selectivity bias and its application to reject inference
Feelders, A. J., Chang, Soong and McLachlan, G. J. (1998). Mining in the presence of selectivity bias and its application to reject inference. 4th International Conference on Knowledge Discovery and Data Mining, New York, United States, 27-31 August 1998. AAAI Press.
Robust cluster analysis via mixtures of multivariate t-distributions
McLachlan G.J. and Peel D. (1998). Robust cluster analysis via mixtures of multivariate t-distributions. 7th Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition, SSPR 1998 and 2nd International Workshop on Statistical Techniques in Pattern Recognition, SPR 1998, August 11, 1998-August 13, 1998. Springer Verlag.
Clustering of magnetic resonance images
McLachlan, GJ, Ng, SK, Galloway, GJ and Wang, D (1996). Clustering of magnetic resonance images. Symposium of the Statistical-Computing-Section, at the Annual Meeting of the American-Statistical-Association, Chicago Il, Aug 04-08, 1996. ALEXANDRIA: AMER STATISTICAL ASSOC.
McLaren, C.E., McLachlan, G.J., Webb, S.J., Jazwinska, E.C., Crawford, D.H.G., Gordeuk, V.R., McLaren, G.D. and Powell, LW (1995). The distribution of transferrin saturation in an asymptomatic Australian population: relevance to the early diagnosis of hemochromatosis Washington, from. December 1-5, 1995.. 37th Annual Meeting of the American Society of Hematology, Seattle, WA, United States, 1-5 December 1995. Washington, DC, United States: American Society of Hematology.
McLaren, CE, McLaren, GD, Kambour, EL, McLachlan, GJ, Lukaski, HC, Li, X and Brittenham, GM (1994). Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests. PHILADELPHIA: SLACK INC.
McLaren, GD, McLaren, CE, Kambour, EL, Lukaski, HC, Xia, L, McLachlan, GJ and Brittenham, GM (1994). Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests. THOROFARE: W B SAUNDERS CO.
O'Brien, M. F., McGiffin, D. C., Stafford, E. G., Gardner, M. A.H., Pohlner, P. F., McLachlan, G. J., Gall, K., Smith, S. and Murphy, E. (1991). Allograft aortic valve replacement: Long-term comparative clinical analysis of the viable cryopreserved and antibiotic 4 °C stored valves. V International Symposium on Cardiac Bioprostheses, Avignon, France, 24–27 May 1991. Hoboken, NJ United States: Wiley-Blackwell. doi: 10.1111/jocs.1991.6.4s.534
Analysis of Some Censored Survival Data From a Large-Scale Study of Melanoma
Holt, JN and McLachlan, GJ (1979). Analysis of Some Censored Survival Data From a Large-Scale Study of Melanoma. WASHINGTON: INTERNATIONAL BIOMETRIC SOC.
Bias Associated with Maximum Likelihood Estimation of Multivariate Logistic Risk Function
McLachlan, GJ (1978). Bias Associated with Maximum Likelihood Estimation of Multivariate Logistic Risk Function. WASHINGTON: INTERNATIONAL BIOMETRIC SOC.
Advances in Data Analysis and Classification
Advances in Data Analysis and Classification. (2015). 9 (4)
Detecting accounting fraud with noisy labels
Ahfock, Daniel, McLachlan, Geoffrey, Yang, Liu and Zhu, Min (2022). Detecting accounting fraud with noisy labels. UQ Business School.
Multivariate analysis: Classification and discriminant analysis
McLachlan, G. J. (2001). Multivariate analysis: Classification and discriminant analysis.
A Novel Approach to Semi-Supervised Statistical Machine Learning
(2023–2026) ARC Discovery Projects
Classification methods for providing personalised and class decisions
(2018–2022) ARC Discovery Projects
ARC Training Centre for Innovation in Biomedical Imaging Technology
(2017–2024) ARC Industrial Transformation Training Centres
Expanding the Role of Mixture Models in Statistical Analyses of Big Data
(2017–2020) ARC Discovery Projects
Power Quality Monitoring of Grids with High Penetration of Power Converters
(2017–2020) ARC Linkage Projects
Gene expression profiling in critically ill patients with septic shock: The ADRENAL-GEPS Study
(2015–2018) NHMRC Project Grant
Large-Scale Statistical Inference: Multiple Testing
(2015–2017) ARC Discovery Projects
Advanced Mixture Models for the Analysis of Modern-Day Data
(2014–2017) ARC Discovery Projects
(2014–2016) ARC Linkage Projects
Joint Clustering and Matching of Multivariate Samples Across Objects
(2012–2014) ARC Discovery Projects
Statistical Modelling of Complex, High-Dimensional Data
(2012–2014) Vice-Chancellor's Senior Research Fellowship
System to synapse: a small animal imaging suite
(2012–2014) UQ Collaboration and Industry Engagement Fund
A New Approach to Fast Matrix Factorization for the Statistical Analysis of High-Dimensional Data
(2011–2013) ARC Discovery Projects
(2008–2010) ARC Discovery Projects
(2007–2011) ARC Discovery Projects
Noncoding RNAs as prognostic markers and therapeutic targets in breast cancer
(2007–2009) NHMRC Project Grant
ARC Network in Imaging Science and Technology
(2004) ARC Seed Funding for Research Networks
ARC Research Network in Microarray Technology
(2004) ARC Seed Funding for Research Networks
ARC Centre of Excellence in Bioinformatics
(2003–2010) ARC Centres of Excellence
Classification of Microarray Gene-Expression Data
(2003) ARC Discovery Projects
Classification of Microarray Gene-expression Data
(2003) UQ External Support Enabling Grant
Unsupervised learning of finite mixture models in data mining applications
(2003) ARC Discovery Projects
Classification of Multiply Observed Features in Terms of Fitted Densities
(2000–2002) ARC Australian Research Council (Large grants)
On Algorithms for the Automatic Analysis and Segmentation of Correlated Images
(2000–2002) ARC Australian Research Council (Large grants)
Artificial Neural Networks and the EM Algorithm
(1999–2001) ARC Australian Research Council (Large grants)
(1999) ARC Australian Research Council (Small grants)
(1998) ARC Australian Research Council (Small grants)
The Analysis of Plant Adaptation Data with Emphasis on Unbalanced Sets
(1997–1999) ARC Australian Research Council (Large grants)
On mixture models in medical imaging
(1997) ARC Australian Research Council (Small grants)
Approximation of multi-dimensional functions for curve fitting and model building
(1995–1997) ARC Australian Research Council (Large grants)
Role of Finite Mixture Models in Semi-Supervised Learning
Doctor Philosophy — Principal Advisor
Other advisors:
The Application of Advanced Statistical Methods to Hyperspectral Images in Mineral Exploration
Doctor Philosophy — Associate Advisor
Other advisors:
Using statistical genetics approaches to gain insight into patterns of variation in complex traits
Doctor Philosophy — Associate Advisor
Other advisors:
Detecting the unexpected in astronomical data using complexity based approaches
(2024) Doctor Philosophy — Principal Advisor
(2023) Doctor Philosophy — Principal Advisor
Model-Based Discriminant Analysis of High-Dimensional Data
(2016) Doctor Philosophy — Principal Advisor
Finite Mixture Models for Regression Problems
(2015) Doctor Philosophy — Principal Advisor
Other advisors:
Finite Mixture Modelling using Multivariate Skew Distributions
(2014) Doctor Philosophy — Principal Advisor
Other advisors:
Detection of Differentially Expressed Genes via Mixture Models and Cluster Analysis
(2012) Doctor Philosophy — Principal Advisor
Other advisors:
Statistical analysis of high-dimensional gene expression data
(2009) Doctor Philosophy — Principal Advisor
CLUSTERING WITH MIXED VARIABLES
(2005) Doctor Philosophy — Principal Advisor
Modelling the statistical behaviour of temperature using a modified Brennan and Schwartz 1982 interest rate model
(2004) Master Science — Principal Advisor
Other advisors:
The Wealth of Features: towards a coherent cooperative game theory for feature importance
(2024) Doctor Philosophy — Associate Advisor
Other advisors:
(2019) Doctor Philosophy — Associate Advisor
Other advisors:
Growth Models and Analysis of Crustacean Growth Data
(2017) Doctor Philosophy — Associate Advisor
(2016) Doctor Philosophy — Associate Advisor
Other advisors:
Genetic Association Studies of Complex Traits
(2013) Doctor Philosophy — Associate Advisor
TOPOLOGICAL MODELS OF TRANSMEMBRANE PROTEINS FOR SUBCELLULAR LOCALIZATION PREDICTION
() Doctor Philosophy — Associate Advisor
Other advisors: