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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-08-7002 |
| Date | 14 January 2010 |
| Creators | Savchuk, Olga |
| Contributors | Hart, Jeffrey D., Sheather, Simon J. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation |
| Format | application/pdf |
Page generated in 0.0021 seconds