Abstract

In the present paper we consider Bayesian estimation of a finite mixture of models with random effects which is also known as the heterogeneity model. First, we discuss the properties of various MCMC samplers that are obtained from full conditional Gibbs sampling by grouping and collapsing. Whereas full conditional Gibbs sampling turns out to be sensitive to the parameterization chosen for the mean structure of the model, the alternative sampler is robust in this respect. However, the logical extension of the approach to the sampling of the group variances does not further increase the efficiency of the sampler. Second, we deal with the identifiability problem due to the arbitrary labeling within the model. Finally, a case study involving metric Conjoint analysis serves as a practical illustration. (author's abstract)
Original languageEnglish
Place of PublicationVienna
PublisherSFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business
Publication statusPublished - 2002

Publication series

NameReport Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
No.72

WU Working Paper Series

  • Report Series SFB \Adaptive Information Systems and Modelling in Economics and Management Science\

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