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Performance of discrete associated kernel estimators through the total variation distance

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  • Kokonendji, Célestin C.
  • Varron, Davit

Abstract

We prove asymptotic results and concentration inequalities for a large class of discrete associated kernel estimators, under the total variation distance. We also propose a data driven bandwidth selection procedure aiming to minimize the total variation. Simulations are conducted.

Suggested Citation

  • Kokonendji, Célestin C. & Varron, Davit, 2016. "Performance of discrete associated kernel estimators through the total variation distance," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 225-235.
    • Handle: RePEc:eee:stapro:v:110:y:2016:i:c:p:225-235
    DOI: 10.1016/j.spl.2015.10.008
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    References listed on IDEAS

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    1. Malec, Peter & Schienle, Melanie, 2014. "Nonparametric kernel density estimation near the boundary," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 57-76.
    2. Kokonendji, Célestin C. & Zocchi, Silvio S., 2010. "Extensions of discrete triangular distributions and boundary bias in kernel estimation for discrete functions," Statistics & Probability Letters, Elsevier, vol. 80(21-22), pages 1655-1662, November.
    3. Chen, Song Xi, 1999. "Beta kernel estimators for density functions," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 131-145, August.
    4. N. Zougab & S. Adjabi & C. Kokonendji, 2012. "Binomial kernel and Bayes local bandwidth in discrete function estimation," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 783-795.
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    Cited by:

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