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• W2015589488 abstract "The purpose of this paper is to investigate data-driven bandwidth selection for non-parametric regression based on a double-smoothing procedure. It will be shown that the best convergence rate can be achieved by kernel regression with non-negative kernels in both pilot smoothing and as well as in main smoothing. The asymptotic results are given for a naive kernel estimator with an equally spaced design, but they can also be used for other kernel estimators or for locally weighted regression. Three variates of data-driven bandwidth selectors for local linear regression are proposed. One of them, ĥ DSI, is root n consistent. The performance of these bandwidth selectors is studied through simulation. They are also compared with the bandwidths selected by the R criterion of Rice and the true ASE optimal bandwidth (h ASE). IN spite of satisfactory performances of all bandwidth selectors, the root n one turns out to be the best in theory as well as in practice." @default.
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• W2015589488 date "1998-01-01" @default.
• W2015589488 modified "2023-09-23" @default.
• W2015589488 title "A simple root n bandwidth selector for nonparametric regression" @default.
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• W2015589488 doi "https://doi.org/10.1080/10485259808832733" @default.
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