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|Title: ||Model selection in regression based on pre-smoothing|
|Authors: ||AERTS, Marc|
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.
|ISI #: ||000281652200003|
|Type: ||Journal Contribution|
|Validation: ||ecoom, 2011|
|Appears in Collections: ||Research publications|
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