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	Record						Links
	Type	Lee, Thomas C. M.
	Publication	ON ALGORITHMS FOR ORDINARY LEAST SQUARES REGRESSION SPLINE FITTING: A COMPARATIVE STUDY	Volume	Journal Article
Pages	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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Serial		URL		ISBN
																							no		NU @ karnesky @		860
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