Testing lattice conditional independence models based on monotone missing data

被引：3
|
作者：
Wu, L
Perlman, MD
机构：
[1] Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA
[2] Univ Washington, Dept Stat, Seattle, WA 98195 USA
来源：
STATISTICS & PROBABILITY LETTERS | 2000年 / 50卷 / 02期
关键词：
likelihood ratio test; multivariate normal data; restricted maximum likelihood estimates;
D O I：
10.1016/S0167-7152(00)00098-5
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Lattice conditional independence (LCI) models (Anderson and Perlman, 1991. Statist. Probab. Lett. 12, 465-486; 1993 Ann. Statist. 21, 1318-1358) can be applied to the analysis of missing data problems with non-monotone missing patterns. Closed-form maximum likelihood estimates can always be obtained under the LCI models naturally determined by the observed data patterns. In practice, it is important to test the appropriateness of LCI models. In the present paper, we derive explicit likelihood ratio tests for testing LCI models based on a monotone subset of the observed data. (C) 2000 Elsevier Science B.V. All rights reserved.
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页码：193 / 201
页数：9
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