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Type Lee, Thomas C. M.
  Publication ON ALGORITHMS FOR ORDINARY LEAST SQUARES REGRESSION SPLINE FITTING: A COMPARATIVE STUDY Volume Journal Article
Pages (down) 2002
  Abstract Journal of Statistical Computation and Simulation  
  Corporate Author  
Publisher 72  
Editor 8
  Summary Language 647-663 Series Editor Bivariate smoothing; Generalized cross-validation; Genetic algorithms; Regression spline; Stepwise selection  
Abbreviated Series Title Regression spline smoothing is a popular approach for conducting nonparametric regression. An important issue associated with it is the choice of a ‘‘theoretically best’’ set of knots. Different statistical model selection methods, such as Akaike’s information criterion and generalized cross-validation, have been applied to derive different ‘‘theoretically best’’ sets of knots. Typically these best knot sets are defined implicitly as the optimizers of some objective functions. Hence another equally important issue concerning regression spline smoothing is how to optimize such objective functions. In this article different numerical algorithms that are designed for carrying out such optimization problems are compared by means of a simulation study. Both the univariate and bivariate smoothing settings will be considered. Based on the simulation results, recommendations for choosing a suitable optimization algorithm under various settings will be provided.
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