Abstract #300287

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JSM 2003 Abstract #300287
Activity Number: 319
Type: Topic Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #300287
Title: Kernel Spline Regression
Author(s): W. John Braun*+
Companies: University of Western Ontario
Address: Western Science Centre, Room 262, London, ON, N6G 5B7, Canada
Keywords: kernel smoothing splines
Abstract:

In this talk, a method of combining spline regression and kernel smoothing is proposed. A simulation study shows that the resulting curve estimators can succeed in capturing the main features of the true curve more effectively than local polynomial regression when the curvature rapidly changes. Kernel splines also exhibit somewhat less sensitivity to choice of bandwidth than local polynomial regression. In many situations, the knots can be chosen manually with some ease. This is an advantage over variable bandwidth and variable order approaches in kernel polynomial regression, which cannot be handled quite as easily. When automatic knot selection is required, the knot deletion method is available. A direct-plug-in bandwidth selector is available as well. Several illustrative examples will also be presented.

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