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

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  • Mynbaev, Kairat
  • Martins-Filho, Carlos

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.

Suggested Citation

  • Mynbaev, Kairat & Martins-Filho, Carlos, 2016. "Reducing bias in nonparametric density estimation via bandwidth dependent kernels: L1 view," MPRA Paper 75902, University Library of Munich, Germany, revised 2016.
  • Handle: RePEc:pra:mprapa:75902
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    File URL: https://mpra.ub.uni-muenchen.de/75902/1/MPRA_paper_75902.pdf
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    References listed on IDEAS

    as
    1. Mynbaev, Kairat T. & Nadarajah, Saralees & Withers, Christopher S. & Aipenova, Aziza S., 2014. "Improving bias in kernel density estimation," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 106-112.
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    More about this item

    Keywords

    Kernel density estimation; higher order kernels; bias reduction;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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