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August 2005 Ignorability for categorical data
Manfred Jaeger
Ann. Statist. 33(4): 1964-1981 (August 2005). DOI: 10.1214/009053605000000363

Abstract

We study the problem of ignorability in likelihood-based inference from incomplete categorical data. Two versions of the coarsened at random assumption (car) are distinguished, their compatibility with the parameter distinctness assumption is investigated and several conditions for ignorability that do not require an extra parameter distinctness assumption are established.

It is shown that car assumptions have quite different implications depending on whether the underlying complete-data model is saturated or parametric. In the latter case, car assumptions can become inconsistent with observed data.

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Manfred Jaeger. "Ignorability for categorical data." Ann. Statist. 33 (4) 1964 - 1981, August 2005. https://doi.org/10.1214/009053605000000363

Information

Published: August 2005
First available in Project Euclid: 5 August 2005

zbMATH: 1078.62002
MathSciNet: MR2166567
Digital Object Identifier: 10.1214/009053605000000363

Subjects:
Primary: 62A01, 62N01

Rights: Copyright © 2005 Institute of Mathematical Statistics

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Vol.33 • No. 4 • August 2005
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