The factoring likelihood method for non-monotone missing data

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We address the problem of parameter estimation in multivariate distributions under ignorable non-monotone missing data. The factoring likelihood method for monotone missing data, termed by Rubin (1974), is applied to a more general case of non-monotone missing data. The proposed method is asymptotically equivalent to the Fisher scoring method from the observed likelihood, but avoids the burden of computing the first and second partial derivatives of the observed likelihood. Instead, the maximum likelihood estimates and their information matrices for each partition of the data set are computed separately and combined naturally using the generalized least squares method. A numerical example is presented to illustrate the method. (c) 2012 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved
Publisher
KOREAN STATISTICAL SOC
Issue Date
2012-09
Language
English
Article Type
Article
Keywords

INCOMPLETE-DATA; MAXIMUM-LIKELIHOOD; REGRESSION; INFERENCE; MODELS

Citation

JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.41, no.3, pp.375 - 386

ISSN
1226-3192
DOI
10.1016/j.jkss.2011.12.003
URI
http://hdl.handle.net/10203/213008
Appears in Collection
MA-Journal Papers(저널논문)
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