Adaptive Bandwidth Choice for Kernel Regression
- 1 June 1995
- journal article
- research article
- Published by JSTOR in Journal of the American Statistical Association
- Vol. 90 (430) , 535
- https://doi.org/10.2307/2291064
Abstract
A data-based procedure is introduced for local bandwidth selection for kernel estimation of a regression function at a point. The estimated bandwidth is shown to be consistent and asymptotically normal as an estimator of the (asymptotic) optimal value for minimum mean square estimation. Simulation studies indicate satisfactory behavior of the new bandwidth estimator in finite samples. The findings are improvements over a global bandwidth estimator. The same methodology works for local linear regression and extends easily to weighted local polynomial fits.Keywords
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