Information attainable in some randomly incomplete multivariate response models

作     者：Desai, Tejas A. Sen, Pranab K. 

作者机构：Indian Inst Management Ahmadabad 380015 Gujarat India Univ N Carolina Dept Biostat Chapel Hill NC 27599 USA Univ N Carolina Dept Stat & Operat Res Chapel Hill NC 27599 USA 

出 版 物：《JOURNAL OF STATISTICAL PLANNING AND INFERENCE》 (统计规划与统计推断杂志)

年 卷 期：2008年第138卷第11期

页      面：3467-3482页

核心收录：

学科分类：07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：maximum likelihood estimates asymptotic optimality EM algorithm SAM algorithm likelihood ratio test statistic 

摘      要：In a general parametric setup, a multivariate regression model is considered when responses may be missing at random while the explanatory variables and covariates are completely observed. Asymptotic optimality properties of maximum likelihood estimators for such models are linked to the Fisher information matrix for the parameters. It is shown that the information matrix is well defined for the missing-at-random model and that it plays the same role as in the complete-data linear models. Applications of the methodologic developments in hypothesis-testing problems, without any imputation of missing data, are illustrated. Some simulation results comparing the proposed method with Rubin s multiple imputation method are presented. (C) 2008 Elsevier B.V. All rights reserved.