Testing lattice conditional independence models based on monotone missing data

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Testing lattice conditional independence models based on monotone missing data

TitleTesting lattice conditional independence models based on monotone missing data
Publication TypeJournal Article
Year of Publication2000
AuthorsWU, LANG, Perlman, MD
JournalStatistics & Probability Letters
Volume50
Pagination193–201
Date Publishednov
ISSN0167-7152
Keywordslikelihood ratio test, Multivariate normal data, Restricted maximum likelihood estimates
AbstractLattice 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.
URLhttp://www.sciencedirect.com/science/article/pii/S0167715200000985
DOI10.1016/S0167-7152(00)00098-5