Publication
Title
Model selection for incomplete and design-based samples
Author
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 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.
Language
English
Source (journal)
Statistics in medicine. - Chichester
Publication
Chichester : 2006
ISSN
0277-6715
Volume/pages
25:14(2006), p. 2502-2520
ISI
000239052300011
Full text (Publishers DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 26.07.2011
Last edited 24.04.2017