Discrete mixtures in Bayesian networks with hidden variables: a latent time budget example

Authors:
J. Croft;J. Q. Smith
Affiliations:
Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK;Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK
Venue:
Computational Statistics & Data Analysis
Year:
2003

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Abstract

The existing methods of analysis applicable to time budget data are summarised. Latent budget models, a subclass of general reduced rank models for two-way contingency tables, are most appropriate as they view each of the observed conditional distributions of interest as a mixture of a small number of conditional distributions involving a hidden variable. However, they suffer from unusually complex unidentifiability problems which can cause standard estimation methods to perform badly and/or be misleading. Recent advances in estimation methods for this type of mixture model which address the unidentifiability issues are reported and demonstrated.