Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: 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
Citation: 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.
URI: http://hdl.handle.net/1942/1469
DOI: 10.1002/sim.2559
ISI #: 000239052300011
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2007
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Published version196.33 kBAdobe PDF
Peer-reviewed author version486.08 kBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.