Summary
Two methods for choosing the smoothing parameter λ (generalized cross-validation, Wahba and Wold, 1975, and robustified generalized cross-validation, Robinson and Moyeed, 1989) are compared in a simulation study. It turns out that robustified cross-validation performs better. Computational problems of finding the cross-validation score are discussed. Findings from linearly transformed data lead to a reduction in costs. In addition we consider problems of variance estimation. Two competitors for error variance estimation turn out to perform equally well, and the estimate for the variance of the Bayesian prior appears to be useful for describing the complexity of the estimated function.
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© 1996 Physica-Verlag Heidelberg
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Neubauer, G.P., Schimek, M.G. (1996). A Note on Cross-Validation for Smoothing Splines. In: Härdle, W., Schimek, M.G. (eds) Statistical Theory and Computational Aspects of Smoothing. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-48425-4_13
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DOI: https://doi.org/10.1007/978-3-642-48425-4_13
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-0930-5
Online ISBN: 978-3-642-48425-4
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