Abstract:
Abstract. This paper proposes a simple and practical iterative method for bias-corrected kernel regression. The proposed approach corrects for both curvature-based and boundary-based finite-sample bias. The method is proposed as an alternative to bias-reduction through the estimation of leading terms in asymptotic expansions, the use of high-order kernels, the use of boundary kernels, or the use of data reflection, and the approach does not require trimming. A cross-validatory approach to bandwidth selection is proposed for this method which yields a bias-corrected estimator with improved square-error properties. In addition to obtaining bias-corrected nonparametric regression functions, bias-corrected confidence bounds and response coefficients can readily be obtained. The technique admits general stationary processes. Simulations and applications are considered.
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