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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/356

Title: Non-Random Missingness in Categorical Data: Strengths and Limitations
Authors: Molenberghs, Geert
Goetghebeur, Els J.T.
Lipsitz, Stuart R.
Kenward, Michael G.
Keywords: Categorical data
Missing data
Longitudinal data
Clustered data
Issue Date: 1999
Publisher: AMER STATISTICAL ASSOC
Citation: The American Statistician, 53(2). p. 110-118
Abstract: 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.
URI: http://hdl.handle.net/1942/356
DOI: 10.2307/2685728
ISI #: 000080223100006
ISSN: 0003-1305
Type: Journal Contribution
Validation: ecoom, 2000
Appears in Collections: Research publications

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