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Reducing variance in nonparametric surface estimation
Authors:Ming-Yen Cheng  Peter Hall  
Affiliation:a Centre for Mathematics and its Applications, Australian National University, Canberra, ACT 0200, Australia;b Department of Mathematics, National Taiwan University, Taipei 106, Taiwan
Abstract:We suggest a method for reducing variance in nonparametric surface estimation. The technique is applicable to a wide range of inferential problems, including both density estimation and regression, and to a wide variety of estimator types. It is based on estimating the contours of a surface by minimising deviations of elementary surface estimates along a quadratic curve. Once a contour estimate has been obtained, the final surface estimate is computed by averaging conventional surface estimates along a portion of the contour. Theoretical and numerical properties of the technique are discussed.
Keywords:Bandwidth   Boundary effect   Kernel method   Nonparametric density estimation   Nonparametric regression   Variance reduction
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