Conjugate generalized linear mixed models with applications

This thesis focuses on the development of conjugate generalized linear mixed models (CGLMMs), which is a computationally efficient modelling framework for longitudinal and multilevel data where the likelihood can be expressed in closed-form. We focus on the scenario where the random effects are mapped uniquely onto the grouping structure and are independent between groups. Compared with conventional inference methods for generalized linear mixed models (GLMMs), CGLMMs allow the parameters to be estimated directly without the need for computational intensive numerical approximation methods. The proposed framework has important implications in terms of distributed computing, privacy preservation in large-scale administrative databases and discrete choice models, which we illustrate using several real data. Altogether, CGLMMs prove to be a credible inference framework and a good alternative to GLMMs, especially when dealing with a large amount of data and/or privacy is of concern.

Author dc:creator

• Lee, Yan Liang (Jarod)

• Linear mixed models.
• Multinomial Regression Models.

Statement dc:rights

• info:eu-repo/semantics/openAccess
• The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
• au.edu.uts.lib/ppc

Language dc:language.iso

en_AU

Handle dc:identifier.uri

http://hdl.handle.net/10453/120352

OAI identifier oai:identifier

oai:opus.lib.uts.edu.au:10453/120352