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November 2010 Block-Conditional Missing at Random Models for Missing Data
Yan Zhou, Roderick J. A. Little, John D. Kalbfleisch
Statist. Sci. 25(4): 517-532 (November 2010). DOI: 10.1214/10-STS344

Abstract

Two major ideas in the analysis of missing data are (a) the EM algorithm [Dempster, Laird and Rubin, J. Roy. Statist. Soc. Ser. B 39 (1977) 1–38] for maximum likelihood (ML) estimation, and (b) the formulation of models for the joint distribution of the data Z and missing data indicators M, and associated “missing at random” (MAR) condition under which a model for M is unnecessary [Rubin, Biometrika 63 (1976) 581–592]. Most previous work has treated Z and M as single blocks, yielding selection or pattern-mixture models depending on how their joint distribution is factorized. This paper explores “block-sequential” models that interleave subsets of the variables and their missing data indicators, and then make parameter restrictions based on assumptions in each block. These include models that are not MAR. We examine a subclass of block-sequential models we call block-conditional MAR (BCMAR) models, and an associated block-monotone reduced likelihood strategy that typically yields consistent estimates by selectively discarding some data. Alternatively, full ML estimation can often be achieved via the EM algorithm. We examine in some detail BCMAR models for the case of two multinomially distributed categorical variables, and a two block structure where the first block is categorical and the second block arises from a (possibly multivariate) exponential family distribution.

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Yan Zhou. Roderick J. A. Little. John D. Kalbfleisch. "Block-Conditional Missing at Random Models for Missing Data." Statist. Sci. 25 (4) 517 - 532, November 2010. https://doi.org/10.1214/10-STS344

Information

Published: November 2010
First available in Project Euclid: 14 March 2011

zbMATH: 1329.62039
MathSciNet: MR2807768
Digital Object Identifier: 10.1214/10-STS344

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.25 • No. 4 • November 2010
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