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Bandwidth selection in nonparametric estimator of density derivative by smoothed cross-validation method. (English. Russian original) Zbl 05790879
Autom. Remote Control 71, No. 2, 209-224 (2010); translation from Avtom. Telemekh. 2010, No. 2, 42-58 (2010).
Summary: In the nonparametric kernel estimation of the unknown probability densities and their derivatives there exist several methods for estimation of the kernel function bandwidth of which the $$CV$$ and $$SCV$$ methods of cross-validation are most simple and suitable. The former method was developed both for the density itself and its derivatives; the latter one, for density only. Yet it generates estimates with a higher rate of convergence and substantially smaller scatter. For the problem of nonparametric restoration of the density derivative from an independent sample, a data-based estimate of the kernel function bandwidth was constructed.

##### MSC:
 62 Statistics
pyuvdata
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##### References:
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