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Adaptive normal reference bandwidth based on quantile for kernel density estimation

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  • Jin Zhang

Abstract

Bandwidth selection is an important problem of kernel density estimation. Traditional simple and quick bandwidth selectors usually oversmooth the density estimate. Existing sophisticated selectors usually have computational difficulties and occasionally do not exist. Besides, they may not be robust against outliers in the sample data, and some are highly variable, tending to undersmooth the density. In this paper, a highly robust simple and quick bandwidth selector is proposed, which adapts to different types of densities.

Suggested Citation

  • Jin Zhang, 2011. "Adaptive normal reference bandwidth based on quantile for kernel density estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(12), pages 2869-2880, March.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2869-2880
    DOI: 10.1080/02664763.2011.570322
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    References listed on IDEAS

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    1. Jin Zhang & Xueren Wang, 2009. "Robust normal reference bandwidth for kernel density estimation," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(1), pages 13-23, February.
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    Cited by:

    1. Jin Zhang, 2015. "Generalized least squares cross-validation in kernel density estimation," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(3), pages 315-328, August.

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