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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/344

Title: Likelihood based frequentist inference when data are missing at random
Authors: Kenward, Michael G.
Molenberghs, Geert
Keywords: Mathematical Statistics
Missing data
Issue Date: 1998
Citation: Statistical Science, 13(3). p. 236-247
Abstract: One of the most often quoted results from the original work of Rubin and Little on the classification of missing value processes is the validity of likelihood based inferences under missing at random (MAR) mechanisms. Although the sense in which this result holds was precisely defined by Rubin, and explored by him in later work, it appears to be now used by some authors in a general and rather imprecise way, particularly with respect to the use of frequentist modes of inference. In this paper an exposition is given of likelihood based frequentist inference under an MAR mechanism that shows in particular which aspects of such inference cannot be separated from consideration of the missing value mechanism. The development is illustrated with three simple setups: a bivariate binary outcome, a bivariate Gaussian outcome and a two-stage sequential procedure with Gaussian outcome and with real longitudinal examples, involving both categorical and continuous outcomes. In particular, it is shown that the classical expected information matrix is biased and the use of the observed information matrix is recommended.
URI: http://hdl.handle.net/1942/344
DOI: 10.1214/ss/1028905886
ISI #: 000077152700003
ISSN: 0883-4237
Type: Journal Contribution
Validation: ecoom, 1999
Appears in Collections: Research publications

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