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Boosting kernel density estimates: A bias reduction technique?

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  • Marco Di Marzio

Abstract

This paper proposes an algorithm for boosting kernel density estimates. We show that boosting is closely linked to a previously proposed method of bias reduction and indicate how it should enjoy similar properties. Numerical examples and simulations are used to illustrate the findings, and we also suggest further areas of research. Copyright Biometrika Trust 2004, Oxford University Press.

Suggested Citation

  • Marco Di Marzio, 2004. "Boosting kernel density estimates: A bias reduction technique?," Biometrika, Biometrika Trust, vol. 91(1), pages 226-233, March.
    • Handle: RePEc:oup:biomet:v:91:y:2004:i:1:p:226-233
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    Cited by:

    1. Kairat Mynbaev & Carlos Martins-Filho, 2010. "Bias reduction in kernel density estimation via Lipschitz condition," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(2), pages 219-235.
    2. Robin, Stephane & Bar-Hen, Avner & Daudin, Jean-Jacques & Pierre, Laurent, 2007. "A semi-parametric approach for mixture models: Application to local false discovery rate estimation," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5483-5493, August.
    3. Alan Huang, 2013. "Density estimation and nonparametric inferences using maximum likelihood weighted kernels," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(3), pages 561-571, September.
    4. Yao, Weixin, 2012. "A bias corrected nonparametric regression estimator," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 274-282.
    5. Masao Ueki & Kaoru Fueda, 2010. "Boosting local quasi-likelihood estimators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(2), pages 235-248, April.
    6. Christopher Withers & Saralees Nadarajah, 2013. "Density estimates of low bias," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(3), pages 357-379, April.

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