Flexible data modelling via skew mixture models:challenges and applications (2016–2019)

Parametric distributions are fundamental to statistical modelling and inference. For centuries, the normal distribution has been the dominant model for continuous data. However, real data rarely satisfy the assumption of normality. There is thus a strong demand for more flexible distributions. This project will develop new methodologies in finite mixture modelling using skew component distributions to provide more adequate models for handling data with non-normal features (such as skewness, heavy/light tails, and multimodality). In particular, challenges in parameter estimation and scalability will be addressed. Key applications will focus on security intrusion detection and clinical diagnosis and prognosis through flow and mass cytometry.
Grant type:
ARC Discovery Early Career Researcher Award
  • Senior Lecturer
    School of Mathematics and Physics
    Faculty of Science
Funded by:
Australian Research Council