Bayesian Methods for Categorical Data Under Informative General Censoring
Carlos Daniel Paulino, Carlos Alberto de Bragança Pereira · Biometrika · 1995
This paper develops a Bayesian approach to the problem of incomplete categorical data informatively censored where the reported sets are not restricted to follow any specific pattern. It generalises that introduced by Paulino & Pereira (1992) in not requiring a censoring pattern by partitions of the set of sampling categories. Some extensions are also discussed.