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The Choice of Weights in Kernel Regression Estimation

Theo Gasser and Joachim Engel
Biometrika
Vol. 77, No. 2 (Jun., 1990), pp. 377-381
Published by: Oxford University Press on behalf of Biometrika Trust
DOI: 10.2307/2336816
Stable URL: http://www.jstor.org/stable/2336816
Page Count: 5
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
The Choice of Weights in Kernel Regression Estimation
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Abstract

For kernel regression estimation a weighting scheme due to Nadaraya and Watson has been associated with random design, and a convolution type weighting scheme with fixed design. Based on integrated mean square error, none of the estimators is uniformly optimal in either design. However, the convolution type weights are minimax optimal. Further advantages of this estimator can be seen in the structure of the bias.

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