Clustering Profiles in Generalized Linear Mixed Models Settings Using Bayesian Nonparametric Statistics

Abstract

Generalized linear mixed models are used to model clustered and longitudinal data in which the distribution of the response variable is a member of the exponential family. This thesis introduces a novel method for simultaneous clustering of such data and estimation of parameters of the underlying generalized linear mixed models.Clustering has been extensively studied for both cross-sectional and longitudinal data. In longitudinal data, one has to take into account the association between observations taken on the same individual. This has found applications in epidemiology, genetics, biology, market research, economics, and many other areas.Generalized linear mixed models consist of two sets of parameters: fixed ef-iv Table of Contents v List of Tables x List of Figures xii List of Tables 2.1 Common distributions in exponential family of distributions . . . . . 14

Constrained Statistical Inference for Categorical Data

“…Originally launched in 2000, and every two-years thereafter, the CCHS modified its reporting in 2007 and began collecting data and publishing its results every year. An extensive redesign of the survey methodology was completed in 2015, and so the data being used for the purposes of this thesis will be 2012 data, which will give us the flexibility of building on the existing works of Karelyn Davis[10] and Predrag Mizdrak[101]. The survey covers Canada's provinces and territories, surveying persons living in private dwellings aged 12 years and older.…”