Title
Model selection in regression based on pre-smoothing Model selection in regression based on pre-smoothing
Author
Faculty/Department
Faculty of Medicine and Health Sciences
Publication type
article
Publication
Abingdon ,
Subject
Mathematics
Source (journal)
Journal of applied statistics. - Abingdon
Volume/pages
37(2010) :9 , p. 1455-1472
ISSN
0266-4763
ISI
000281652200003
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
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.
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