dc.contributor.advisor | Hart, Jeffrey D. | |
dc.contributor.advisor | Sheather, Simon J. | |
dc.creator | Savchuk, Olga | |
dc.date.accessioned | 2010-01-15T00:16:54Z | |
dc.date.accessioned | 2010-01-16T00:13:52Z | |
dc.date.available | 2010-01-15T00:16:54Z | |
dc.date.available | 2010-01-16T00:13:52Z | |
dc.date.created | 2009-08 | |
dc.date.issued | 2010-01-14 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/ETD-TAMU-2009-08-7002 | |
dc.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. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.subject | bandwidth selection | en |
dc.subject | cross-validation | en |
dc.subject | kernel density estimation | en |
dc.subject | kernel regression | en |
dc.subject | nonparametric function estimation | en |
dc.title | Choosing a Kernel for Cross-Validation | en |
dc.type | Book | en |
dc.type | Thesis | en |
thesis.degree.department | Statistics | en |
thesis.degree.discipline | Statistics | en |
thesis.degree.grantor | Texas A&M University | en |
thesis.degree.name | Doctor of Philosophy | en |
thesis.degree.level | Doctoral | en |
dc.contributor.committeeMember | Subba Rao, Suhasini | |
dc.contributor.committeeMember | Li, Qi | |
dc.type.genre | Electronic Dissertation | en |