Model selection in regression based on pre-smoothing

Marc Aerts, Niel Hens and Jeffrey Simonoff

Journal of Applied Statistics, 2010, vol. 37, issue 9, 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.

Keywords: Akaike information criterion; fractional polynomial; latent variable model; model selection; pre-smoothing (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (1)

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DOI: 10.1080/02664760903046086

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