Model Selection in Regression Based on Presmoothing

24 Pages Posted: 3 Nov 2008

See all articles by Marc Aerts

Marc Aerts

affiliation not provided to SSRN

Niel Hens

affiliation not provided to SSRN

Jeffrey S. Simonoff

New York University (NYU) - Leonard N. Stern School of Business; New York University (NYU) - Department of Information, Operations, and Management Sciences

Date Written: 2006

Abstract

In this paper we investigate the effect of presmoothing on model selection. ChristobalChristobal et al. (1987) showed the beneficial effect of presmoothing for 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 tocontrol the magnitude of the random error structure in the smoothed data. Theeffect of presmoothing 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, Presmoothing

Suggested Citation

Aerts, Marc and Hens, Niel and Simonoff, Jeffrey S., Model Selection in Regression Based on Presmoothing (2006). Statistics Working Papers Series, Vol. , pp. -, 2006. Available at SSRN: https://ssrn.com/abstract=1293150

Marc Aerts (Contact Author)

affiliation not provided to SSRN

No Address Available

Niel Hens

affiliation not provided to SSRN

No Address Available

Jeffrey S. Simonoff

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

New York University (NYU) - Department of Information, Operations, and Management Sciences

44 West Fourth Street
New York, NY 10012
United States

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