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Title
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Model selection for incomplete and design-based samples
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Author
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Abstract
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| The Akaike information criterion, AIC, is one of the most frequently used methods to select one or a few good, optimal regression models from a set of candidate models. In case the sample is incomplete, the naive use of this criterion on the so-called complete cases can lead to the selection of poor or inappropriate models. A similar problem occurs when a sample based on a design with unequal selection probabilities, is treated as a simple random sample. In this paper, we consider a modification of AIC, based on reweighing the sample in analogy with the weighted HorvitzThompson estimates. It is shown that this weighted AIC-criterion provides better model choices for both incomplete and design-based samples. The use of the weighted AIC-criterion is illustrated on data from the Belgian Health Interview Survey, which motivated this research. Simulations show its performance in a variety of settings. |
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Language
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English
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Source (journal)
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Statistics in medicine. - Chichester
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Publication
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Chichester
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2006
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ISSN
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0277-6715
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DOI
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10.1002/SIM.2559
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Volume/pages
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25
:14
(2006)
, p. 2502-2520
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ISI
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000239052300011
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Full text (Publisher's DOI)
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