科研之友
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摘要
Since the late 1980s, several methods have been considered in the literature to reduce the sample variability of the least-squares cross-validation bandwidth selector for kernel density estimation. In this article, a weighted version of this classical method is proposed and its asymptotic and finite-sample behavior is studied. The simulation results attest that the weighted cross-validation bandwidth performs quite well, presenting a better finite-sample performance than the standard cross-validation method for "easy-to-estimate" densities, and retaining the good finite-sample performance of the standard cross-validation method for "hard-to-estimate" ones.
- 出版日期2017
