Bayesian analysis of categorical data informatively censored

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.

Keywords:

• Bayesian operation
• Dirichlet and generalized Dirichlet distributions
• incomplete categorical data
• informative censoring process

AMS classification:

• 62A15
• 62H17
• 62C10