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-va...
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| Format: | Others |
| Language: | en_US |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-7002 http://hdl.handle.net/1969.1/ETD-TAMU-2009-08-7002 |
| Summary: | 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. |
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