Estimating the smoothing parameter in generalized spline-based regression

Angelika Linde
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Angelika Linde: University of Bremen

Computational Statistics, 2001, vol. 16, issue 1, No 3, 43-71

Abstract: Summary Estimation of a smooth predictor function in logistic regression requires the determination of a smoothing parameter. Several cross-validatory criteria for finding such a smoothing parameter have been proposed generalizing techniques that are asymptotically well performing for Gaussian data. Here it is argued that a smoothing parameter is a model parameter and can be estimated cross-validating model fit criteria for generalized regression models taking explicitly into account the non-Gaussian distribution of the observed variables. Several criteria based on model choice for binary data are introduced and their performance is investigated in a simulation study where smooth predictor functions are estimated by smoothing splines. The empirical results indicate that cross-validated model fit criteria perform well.

Keywords: nonparametric regression; splines; cross-validation; power-divergence statistics (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1007/s001800100051

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