Journal of the Korean Statistical Society

The Korean Statistical Society (한국통계학회)
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Bayesian Parameter :Estimation and Variable Selection in Random Effects Generalised Linear Models for Count Data

  • Oh, Man-Suk (Department of Statistics, Ewha Womans University) ;
  • Park, Tae-Sung (Department of Statistics, Seoul National University)
  • Published : 2002.03.01
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Abstract

Random effects generalised linear models are useful for analysing clustered count data in which responses are usually correlated. We propose a Bayesian approach to parameter estimation and variable selection in random effects generalised linear models for count data. A simple Gibbs sampling algorithm for parameter estimation is presented and a simple and efficient variable selection is done by using the Gibbs outputs. An illustrative example is provided.

Keywords

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