ABSTRACT

This chapter highlights some applications of Generalized linear mixed models (GLMM) to Mixture modelmixture models. Extending the Generalized linear model there were vast literature exploring and developing statistical methods in a direction to relax the independence of the response variable assumption. Sometimes, such data dependence may even exhibit a Hierarchical structurehierarchical clustering structure. Generalized linear model (GLM) has provided a unified framework for a variety of response data types. A natural extension is the generalization of GLM by incorporating Random effectsrandom effects such that the dependence structure can explicitly be modelled. One of the important applications of GLMM is on Frailty modelfrailty models in Survival analysissurvival analysis. In essence, GLMM built on the developments in GLM and linear mixed models, and merged into an advanced modelling technique which allowed non-Gaussian response and relaxation of the independence assumption on the response variable.