Reducing bias in nonparametric density estimation via bandwidth dependent kernels: L1 view

Kairat Mynbaev and Carlos Martins-Filho

MPRA Paper from University Library of Munich, Germany

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 L1. No additional assumptions are imposed to the extant literature.

Keywords: Kernel density estimation; higher order kernels; bias reduction (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2016, Revised 2016
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Related works:
Journal Article: Reducing bias in nonparametric density estimation via bandwidth dependent kernels: L1 view (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:75902

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