Some model-restricted shrinkage estimators for contingency tables with missing data

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Some model-restricted shrinkage estimators for contingency tables with missing data

TitleSome model-restricted shrinkage estimators for contingency tables with missing data
Publication TypeJournal Article
Year of Publication2000
AuthorsWU, LANG, Reeves, J, Perlman, MD
JournalMetrika
Volume50
Pagination221–245
Date Publishedapr
ISSN0026-1335, 1435-926X
KeywordsEM algorithm, empirical Bayes estimator, Key words: Sparse data, lattice conditional independence model, restricted maximum likelihood estimator
Abstract. For contingency tables with extensive missing data, the unrestricted MLE under the saturated model, computed by the EM algorithm, is generally unsatisfactory. In this case, it may be better to fit a simpler model by imposing some restrictions on the parameter space. Perlman and Wu (1999) propose lattice conditional independence (LCI) models for contingency tables with arbitrary missing data patterns. When this LCI model fits well, the restricted MLE under the LCI model is more accurate than the unrestricted MLE under the saturated model, but not in general. Here we propose certain empirical Bayes (EB) estimators that adaptively combine the best features of the restricted and unrestricted MLEs. These EB estimators appear to be especially useful when the observed data is sparse, even in cases where the suitability of the LCI model is uncertain. We also study a restricted EM algorithm (called the ER algorithm) with similar desirable features.
URLhttp://link.springer.com/article/10.1007/s001840050047
DOI10.1007/s001840050047