Discipline: 
Mathematics
Status: 
Available
Level: 
Masters Project
Level: 
Honours Project
Supervisor(s): 
Dr. Alan Huang

Generalized linear models have quickly become indispensable tools for the analysis of biomedical, agricultural and engineering data. Recently, there has been some exciting work on novel extensions of generalized linear models that relax classical structural and distributional assumptions, giving them increased flexibility in handling a wider range of scenarios. This project will look at some theoretical and computational aspects of these extensions, leading to valuable contributions to the existing knowledge in regression modelling. The project will be suitable for a student with a Statistical or Computer Science background.