Choosing a kernel for cross-validation /

Bibliographic Details
Main Author:	Savchuk, Olga
Other Authors:	Hart, Jeffrey D. (Thesis advisor), Sheather, Simon J. (Thesis advisor)
Format:	Thesis eBook
Language:	English
Published:	[College Station, Tex.] : [Texas A&M University], [2010]
Subjects:	Major statistics. Bandwidth selection Nonparametric function estimation Kernel density estimation Kernel regression Cross-validation
Online Access:	Link to OAK Trust copy

Description
Abstract:	The statistical properties of cross-validation bandwidths can be improved by choosing an appropriate kernel, which is different from the kernels traditionally used for cross- validation purposes. In the light of this idea, we developed two new methods of bandwidth selection termed: Indirect cross-validation and Robust one-sided cross- validation. The kernels used in the Indirect cross-validation method yield an improvement in the relative bandwidth rate to n^⁻1/4, which is substantially better than the n^⁻1/10 rate of the least squares cross-validation method. The robust kernels used in the Robust one-sided cross-validation method eliminate the bandwidth bias for the case of regression functions with discontinuous derivatives.
Item Description:	"Major Subject: Statistics" Title from author supplied metadata (automated record created 2010-03-12 12:08:51). Electronic resource.
Physical Description:	1 online resource.
Bibliography:	Includes bibliographical references.