Bias reduction in kernel density estimation via Lipschitz condition
Kairat Mynbaev () and
MPRA Paper from University Library of Munich, Germany
In this paper we propose a new nonparametric kernel based estimator for a density function $f$ which achieves bias reduction relative to the classical Rosenblatt-Parzen estimator. Contrary to some existing estimators that provide for bias reduction, our estimator has a full asymptotic characterization including uniform consistency and asymptotic normality. In addition, we show that bias reduction can be achieved without the disadvantage of potential negativity of the estimated density - a deficiency that results from using higher order kernels. Our results are based on imposing global Lipschitz conditions on $f$ and defining a novel corresponding kernel. A Monte Carlo study is provided to illustrate the estimator's finite sample performance.
Keywords: bias reduction; kernel density estimation; Lipschitz conditions (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
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Published in Journal of Nonparametric Statistics 2.22(2010): pp. 219-235
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Journal Article: Bias reduction in kernel density estimation via Lipschitz condition (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:24904
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