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May 2008 Formal and Informal Model Selection with Incomplete Data
Geert Verbeke, Geert Molenberghs, Caroline Beunckens
Statist. Sci. 23(2): 201-218 (May 2008). DOI: 10.1214/07-STS253


Model selection and assessment with incomplete data pose challenges in addition to the ones encountered with complete data. There are two main reasons for this. First, many models describe characteristics of the complete data, in spite of the fact that only an incomplete subset is observed. Direct comparison between model and data is then less than straightforward. Second, many commonly used models are more sensitive to assumptions than in the complete-data situation and some of their properties vanish when they are fitted to incomplete, unbalanced data. These and other issues are brought forward using two key examples, one of a continuous and one of a categorical nature. We argue that model assessment ought to consist of two parts: (i) assessment of a model’s fit to the observed data and (ii) assessment of the sensitivity of inferences to unverifiable assumptions, that is, to how a model described the unobserved data given the observed ones.


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Geert Verbeke. Geert Molenberghs. Caroline Beunckens. "Formal and Informal Model Selection with Incomplete Data." Statist. Sci. 23 (2) 201 - 218, May 2008.


Published: May 2008
First available in Project Euclid: 21 August 2008

zbMATH: 1327.62027
MathSciNet: MR2516820
Digital Object Identifier: 10.1214/07-STS253

Rights: Copyright © 2008 Institute of Mathematical Statistics


Vol.23 • No. 2 • May 2008
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