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Estimating the smoothing parameter in generalized spline-based regression

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  • Angelika Linde

    (University of Bremen)

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.

Suggested Citation

  • Angelika Linde, 2001. "Estimating the smoothing parameter in generalized spline-based regression," Computational Statistics, Springer, vol. 16(1), pages 43-71, March.
    • Handle: RePEc:spr:compst:v:16:y:2001:i:1:d:10.1007_s001800100051
    DOI: 10.1007/s001800100051
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    Cited by:

    1. van der Linde, Angelika, 2008. "Variational Bayesian functional PCA," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 517-533, December.

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