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Article Dans Une Revue Electronic Journal of Statistics Année : 2021

On a Nadaraya-Watson Estimator with Two Bandwidths

Fabienne Comte
Nicolas Marie

Résumé

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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Dates et versions

hal-02457079 , version 1 (27-01-2020)
hal-02457079 , version 2 (25-04-2021)

Identifiants

Citer

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