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The factoring likelihood method for non-monotone missing data

Title
The factoring likelihood method for non-monotone missing data
Authors
Kim J.K.Shin D.W.
Ewha Authors
신동완
SCOPUS Author ID
신동완scopus
Issue Date
2012
Journal Title
Journal of the Korean Statistical Society
ISSN
1226-3192JCR Link
Citation
vol. 41, no. 3, pp. 375 - 386
Indexed
SCIE; SCOPUS; KCI WOS scopus
Abstract
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. © 2012 The Korean Statistical Society.
DOI
10.1016/j.jkss.2011.12.003
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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