We propose a class of unbiased and strongly consistent nonparametric kernel estimates of a probability density function, based on a random choice of the sample size and the kernel function. The expected sample size can be arbitrarily small and mild conditions on the local behavior of the density function are imposed.
@article{bwmeta1.element.bwnjournal-article-zmv22z4p485bwm,
author = {Tomasz Rychlik},
title = {A class of unbiased kernel estimates of a probability density function},
journal = {Applicationes Mathematicae},
volume = {23},
year = {1995},
pages = {485-497},
zbl = {0814.62022},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-zmv22z4p485bwm}
}
Rychlik, Tomasz. A class of unbiased kernel estimates of a probability density function. Applicationes Mathematicae, Tome 23 (1995) pp. 485-497. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-zmv22z4p485bwm/
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