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 (Publisher's DOI)
|
|
|
|
| |
|