Problems of automatic data-driven bandwidth selectors for nonparametric regression
| Authors | |
|---|---|
| Year of publication | 2002 |
| Type | Article in Periodical |
| Magazine / Source | Journal of Electrical Engineering |
| MU Faculty or unit | |
| Citation | |
| Field | Applied statistics, operation research |
| Keywords | Nonparametric regression; data driven bandwidth selector; Fourier transformation |
| Description | This note is concerned with the problem of automatic data-driven bandwidth selectors for nonparametric regression. Some selectors were shown to be consistent and asymptotically unbiased by Rice (1984) and H\"ardle (1990). However, in simulation studies, it is often observed that most selectors are biased toward undersmoothing and give smaller bandwidths more frequently than predicted by asymptotic results.This motivates us to study the causes of undersmoothing. An explanation for the difficulty is given here. The Fourier transformation is used for a remedy. This leads to the consideration of a new procedure which is simple modification of a classical selector. A simulation study suggests that the proposed selector is much more consistent than the classical one. |
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