Bayesian analysis of categorical data informatively censored |
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Authors: | Carlos Daniel Mimoso Paulino Carlos Alberto De Bragança Pereira |
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Institution: | 1. Dep. de Matemática , Univ. Técnica de Lisboa , IST Av. Rovisco Pais, Lisboa CODEX, 1096, Portugal;2. Dep. de Estatistica , Universidade de Sào Paulo , IME Caixa Postal 20570 Ag. Iguatemi, S?o Paulo, 01498, Brasil |
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Abstract: | This article presents a general Bayesian analysis of incomplete categorical data considered as generated by a statistical model involving the categorical sampling process and the observable censoring process. The novelty is that we allow dependence of the censoring process paramenters on the sampling categories; i.e., an informative censoring process. In this way, we relax the assumptions under which both classical and Bayesian solutions have been de-veloped. The proposed solution is outlined for the relevant case of the censoring pattern based on partitions. It is completely developed for a simple but typical examples. Several possible extensions of our procedure are discussed in the final remarks. |
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Keywords: | Bayesian operation Dirichlet and generalized Dirichlet distributions incomplete categorical data informative censoring process |
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