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Tenreiro, C.
A weighted least-squares cross-validation bandwidth selector for kernel density estimation. (English) Zbl 1368.62089
Commun. Stat., Theory Methods 46, No. 7, 3438-3458 (2017).
Summary: 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.

Cited in 2 Documents
MSC:
62G07 Density estimation
62G20 Asymptotic properties of nonparametric inference
Keywords:
kernel density estimation; bandwidth selection; cross-validation
Software:
R
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\textit{C. Tenreiro}, Commun. Stat., Theory Methods 46, No. 7, 3438--3458 (2017; Zbl 1368.62089)
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