Journal
COMPUTATIONAL STATISTICS
Volume 15, Issue 4, Pages 443-462Publisher
PHYSICA-VERLAG GMBH & CO
DOI: 10.1007/s001800000047
Keywords
Bayesian variable selection; B-spline; Gibbs sampling; nonparametric regression; polynomial spline; roughness penalty; stepwise regression
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Regression spline smoothing involves modelling a regression function as a piecewise polynomial with a high number of pieces relative to the sample size. Because the number of possible models is so large, efficient strategies for choosing among them are required. In this paper we review approaches to this problem and compare them through a simulation study. For simplicity and conciseness we restrict attention to the univariate smoothing setting with Gaussian noise and the truncated polynomial regression spline basis.
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