Reducing variance in nonparametric surface estimation
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.
Year of publication: |
2003
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Authors: | Cheng, Ming-Yen ; Hall, Peter |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 86.2003, 2, p. 375-397
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Publisher: |
Elsevier |
Keywords: | Bandwidth Boundary effect Kernel method Nonparametric density estimation Nonparametric regression Variance reduction |
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