Lattice conditional independence models for contingency tables with non-monotone missing data patterns

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Lattice conditional independence models for contingency tables with non-monotone missing data patterns

TitleLattice conditional independence models for contingency tables with non-monotone missing data patterns
Publication TypeJournal Article
Year of Publication1999
AuthorsPerlman, MD, WU, LANG
JournalJournal of Statistical Planning and Inference
Volume79
Pagination259–287
Date Publishedjul
ISSN0378-3758
AbstractIn the analysis of non-monotone missing data patterns in multinomial distributions for contingency tables, it is known that explicit MLEs of the unknown parameters cannot be obtained. Iterative procedures such as the EM-algorithm are therefore required to obtain the MLEs. These iterative procedures, however, may offer several potential difficulties. Andersson and Perlman [Ann. Statist. 21 (1993) 1318–1358] introduced lattice conditional independence (LCI) models for multivariate normal distributions, which can be applied to the analysis of non-monotone missing observations in continuous data (Andersson and Perlman, Statist. Probab. Lett. 12 (1991) 465–486). In this paper, we show that LCI models may also be applied to the analysis of categorical data with non-monotone missing data patterns. Under a parsimonious set of LCI assumptions naturally determined by the observed data pattern, the likelihood function for the observed data can be factored as in the monotone case and explicit MLEs can be obtained for the unknown parameters. Furthermore, the LCI assumptions can be tested by explicit likelihood ratio tests.
URLhttp://www.sciencedirect.com/science/article/pii/S0378375899000038
DOI10.1016/S0378-3758(99)00003-8