抄録
Data which have hierarchical structure are observed in many fields like a sociology, psychology and clinical trials. Hierarchical Generalized Linear Models ( HGLM ) is applied to the hierarchical data to carry out analyses taking account of the data structure. Likelihood (and approximate likelihood) approaches based on asymptotic theory are most widely used in current hierarchical analyses. One of alternative approaches is Bayesian approach. As well known Bayesian approach will be quite robust even when the target data size is small. Purpose of this research is to compare Bayesian and likelihood-based approaches for fitting of Hierarchical generalized linear model.
