On a Nadaraya-Watson estimator with two bandwidths

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.

Automated summary

In this study, a bandwidth selection method is proposed for estimating the regression function using kernel estimators. The simulation study demonstrated that the single-bandwidth cross-validation estimator performs better in small noise context.