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The Choice of Weights in Kernel Regression Estimation
Theo Gasser and Joachim Engel
Vol. 77, No. 2 (Jun., 1990), pp. 377-381
Stable URL: http://www.jstor.org/stable/2336816
Page Count: 5
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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.
Biometrika © 1990 Biometrika Trust