Reducing bias in nonparametric density estimation via bandwidth dependent kernels: $L_1$ view
Statistics Theory
2016-12-28 v1 Statistics Theory
Abstract
We define a new bandwidth-dependent kernel density estimator that improves existing convergence rates for the bias, and preserves that of the variation, when the error is measured in . No additional assumptions are imposed to the extant literature.
Cite
@article{arxiv.1611.10203,
title = {Reducing bias in nonparametric density estimation via bandwidth dependent kernels: $L_1$ view},
author = {Kairat Mynbaev and Carlos Martins-Filho},
journal= {arXiv preprint arXiv:1611.10203},
year = {2016}
}
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9 pages