Publication
Title
Model selection in regression based on pre-smoothing
Author
Abstract
In this paper, we investigate the effect of pre-smoothing on model selection. Christobal et al 6 showed the beneficial effect of pre-smoothing on estimating the parameters in a linear regression model. Here, in a regression setting, we show that smoothing the response data prior to model selection by Akaike's information criterion can lead to an improved selection procedure. The bootstrap is used to control the magnitude of the random error structure in the smoothed data. The effect of pre-smoothing on model selection is shown in simulations. The method is illustrated in a variety of settings, including the selection of the best fractional polynomial in a generalized linear model.
Language
English
Source (journal)
Journal of applied statistics. - Abingdon
Publication
Abingdon : 2010
ISSN
0266-4763
Volume/pages
37:9(2010), p. 1455-1472
ISI
000281652200003
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 26.07.2011
Last edited 05.07.2017
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