Nonrandom missingness in categorical data: Strengths and limitations

Molenberghs, G. and Goetghebeur, E.J.T. and Lipsitz, S.R. and Kenward, Michael G. (1999) Nonrandom missingness in categorical data: Strengths and limitations. American Statistician, 53 (2). pp. 110-118. ISSN 0003-1305. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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There have recently been substantial developments in the analysis of incomplete data. Modeling tools are now available for nonrandom missingness and these methods are finding their way into the broad statistical community. The computational and interpretational issues that surround such models are less well known. This article provides an exposition of several of these issues in a categorical data setting. It is argued that the use of contextual information can aid the modeler in discriminating among models that are indistinguishable purely on statistical grounds.

Item Type: Article
Uncontrolled keywords: contingency tables; generalized linear models; longitudinal data; maximum likelihood estimation; missing values; nonrandom missingness
Subjects: H Social Sciences > HA Statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Physical Sciences
Depositing User: I.T. Ekpo
Date Deposited: 05 Apr 2009 22:00
Last Modified: 11 Jul 2014 09:19
Resource URI: (The current URI for this page, for reference purposes)
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