A Bayesian maximum a posteriori algorithm for categorical data under informative general censoring

作     者：Walker, S 

作者机构：UNIV LONDON IMPERIAL COLL SCI TECHNOL & MED DEPT MATH LONDON SW7 2BZ ENGLAND 

出 版 物：《JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN》 (J. R. Stat. Soc. Ser. D Stat.)

年 卷 期：1996年第45卷第3期

页      面：293-298页

核心收录：

主　　题：EM algorithm generalized Dirichlet distribution informative censoring maximum a posteriori estimation 

摘      要：An EM algorithm to obtain maximum a posteriori estimates ibr incomplete categorical data under informative general censoring is presented. It is an alternative version to the Bayesian approach described by Paulino and Pereira but which allows more general prior specifications.