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Choosing a Kernel for Cross-Validation

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.

Identiferoai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-08-7002
Date14 January 2010
CreatorsSavchuk, Olga
ContributorsHart, Jeffrey D., Sheather, Simon J.
Source SetsTexas A and M University
Languageen_US
Detected LanguageEnglish
TypeBook, Thesis, Electronic Dissertation
Formatapplication/pdf

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