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http://hdl.handle.net/1942/1469
Title: | Model selection for incomplete and design-based samples | Authors: | HENS, Niel AERTS, Marc MOLENBERGHS, Geert |
Issue Date: | 2006 | Source: | STATISTICS IN MEDICINE, 25(14). p. 2502-2520 | Abstract: | 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 Horvitz-Thompson 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. Copyright (c) 2006 John Wiley & Sons, Ltd. | Keywords: | missing data; weighted likelihood; model selection; complex designs; Akaike information criterion; WEIGHTED LIKELIHOOD METHODOLOGY; AKAIKE INFORMATION CRITERION; ESTIMATING EQUATIONS; REGRESSION; 2-STAGE; FIT;missing data; weighted likelihood; model selection; complex designs; Akaike information criterion | Document URI: | http://hdl.handle.net/1942/1469 | ISSN: | 0277-6715 | e-ISSN: | 1097-0258 | DOI: | 10.1002/sim.2559 | ISI #: | 000239052300011 | Rights: | (C) 2006 John Wiley & Sons, Ltd. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2007 |
Appears in Collections: | Research publications |
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hens2006.pdf Restricted Access | Published version | 196.33 kB | Adobe PDF | View/Open Request a copy |
Model_selection_for_incomplete_and_desig.pdf | Peer-reviewed author version | 473.08 kB | Adobe PDF | View/Open |
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