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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11222

Title: Model selection in regression based on pre-smoothing
Authors: AERTS, Marc
HENS, Niel
Simonoff, Jeffrey S.
Issue Date: 2010
Publisher: ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
Citation: JOURNAL OF APPLIED STATISTICS, 37 (9). p. 1455-1472
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.
Notes: [Aerts, Marc; Hens, Niel] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Simonoff, Jeffrey S.] NYU, Leonard N Stern Sch Business, New York, NY 10012 USA. marc.aerts@uhasselt.be
URI: http://hdl.handle.net/1942/11222
DOI: 10.1080/02664760903046086
ISI #: 000281652200003
ISSN: 0266-4763
Category: A1
Type: Journal Contribution
Validation: ecoom, 2011
Appears in Collections: Research publications

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