You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. 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
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
You can always find the topics here!Topics: Estimators, Estimation bias, Minimax, Density estimation, Linear regression, Estimators for the mean, Statistical variance, Statistical estimation, Term weighting, Linear transformations
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
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.
Preview not available
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