Abstract
In this paper we propose a variable bandwidth kernel regression estimator fori.i.d. observations in ℝ2to improve the classical Nadaraya-Watson estimator. The bias is improved to the order ofO(hn4) under the condition that the fifth order derivative of the density function and the sixth order derivative of the regression function are bounded and continuous. We also establish the central limit theorems for the proposed ideal and true variable kernel regression estimators. The simulation study confirms our results and demonstrates the advantage of the variable bandwidth kernel method over the classical kernel method.
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