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Biometrika 1999 86(4):936-940; doi:10.1093/biomet/86.4.936
© 1999 by Biometrika Trust
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Miscellanea. On the optimal amount of smoothing in penalised spline regression

MP Wand

Department of Biostatistics, School of Public Health, Harvard University, 665 Huntington Avenue, Boston, MA 02115, USA E-mail: mwand@hsph.harvard.edu

The optimal amount of smoothing in penalised spline regression is investigated. In particular, a simple closed form approximation to the optimal smoothing parameter is derived. Comparisons with its exact counterpart show it to be a useful starting point for measuring the optimal amount of smoothing in penalised spline regression. It also lends itself to the development of quick and simple rules for automatic smoothing parameter selection.

Key Words: asymptotic approximation; automatic smoothing parameter selection; nonparametric regression; quick and simple smoothing parameter selection; regression spline


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