Markov Chain Monte Carlo Simulation in Dynamic Generalized Linear Mixed Models
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چکیده
Dynamic generalized linear mixed models are proposed as a regression tool for nonnormal longitudinal data This framework is an interesting combination of dynamic models by other name state space models and mixed models also known as random e ect models The main feature is that both time and unit speci c parameters are allowed which is especially attractive if a considerable number of units is ob served over a longer period Statistical inference is done by means of Markov chain Monte Carlo techniques in a full Bayesian setting The algorithm is based on iterative updating using full conditionals Due to the hierarchical structure of the model and the extensive use of Metropolis Hastings steps for updating this algorithm mainly eva luates log likelihoods in multivariate normal distributed proposals It is derivative free and covers a wide range of di erent models inclu ding dynamic and mixed models the latter with slight modi cations The methodology is illustrated through an analysis of arti cial binary data and multicategorical business test data Some key words Bayesian inference Generalized linear model Heteroge neity Longitudinal data Markov chain Monte Carlo Metropolis Hastings algorithm Time varying regression parameters Email leo stat uni muenchen de Last corrections July
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