Bandwidth matrix selectors for kernel regression
| Authors | |
|---|---|
| Year of publication | 2017 |
| Type | Article in Periodical |
| Magazine / Source | Computational Statistics |
| MU Faculty or unit | |
| Citation | |
| Web | http://is.muni.cz/auth/repo/1319858/template_cost.pdf |
| Doi | http://dx.doi.org/10.1007/s00180-017-0709-3 |
| Field | General mathematics |
| Keywords | multivariate kernel regression; constrained bandwidth matrix; kernel smoothing; mean integrated square error |
| Attached files | |
| Description | Choosing a bandwidth matrix belongs to the class of significant problems in multivariate kernel regression. The problem consists of the fact that a theoretical optimal bandwidth matrix depends on the unknown regression function which to be estimated. Thus data-driven methods should be applied. A method proposed here is based on a relation between asymptotic integrated square bias and asymptotic integrated variance. Statistical properties of this method are also treated. The last two sections are devoted to simulations and an application to real data. |
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