Theory for penalised spline regression
1 Centre for Mathematics and its Applications, Australian National University, Canberra, ACT 0200, Australia peter.hall{at}anu.edu.au, 2 Department of Statistics, Iowa State University, Ames, Iowa 50011, U.S.A. jopsomer{at}iastate.edu
Penalised spline regression is a popular new approach to smoothing, but its theoretical properties are not yet well understood. In this paper, mean squared error expressions and consistency results are derived by using a white-noise model representation for the estimator. The effect of the penalty on the bias and variance of the estimator is discussed, both for general splines and for the case of polynomial splines. The penalised spline regression estimator is shown to achieve the optimal nonparametric convergence rateestablished by Stone (1982).
Key Words: Nonparametric regression; White noise model
Received October 2003. Revised May 2004.
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