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On a Nadaraya-Watson Estimator with Two Bandwidths

Abstract : In a regression model, we write the Nadaraya-Watson estimator of the regression function as the quotient of two kernel estimators, and propose a bandwidth selection method for both the numerator and the denominator. We prove risk bounds for both data driven estimators and for the resulting ratio. The simulation study confirms that both estimators have good performances, compared to the ones obtained by cross-validation selection of the bandwidth. However, unexpectedly, the single-bandwidth cross-validation estimator is found to be much better than the ratio of the previous two good estimators, in the small noise context. However, the two methods have similar performances in models with large noise.
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https://hal.archives-ouvertes.fr/hal-02457079
Contributor : Nicolas Marie Connect in order to contact the contributor
Submitted on : Sunday, April 25, 2021 - 10:59:48 PM
Last modification on : Friday, April 1, 2022 - 3:56:04 AM

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Fabienne Comte, Nicolas Marie. On a Nadaraya-Watson Estimator with Two Bandwidths. Electronic Journal of Statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2021, 15 (1), pp.2566-2607. ⟨10.1214/21-EJS1849⟩. ⟨hal-02457079v2⟩

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