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Robust normal reference bandwidth for kernel density estimation

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  • Jin Zhang
  • Xueren Wang

Abstract

Bandwidth selection is the main problem of kernel density estimation, the most popular method of density estimation. The classical normal reference bandwidth usually oversmoothes the density estimate. The existing hi‐tech bandwidths have computational problems (even may not exist) and are not robust against outliers in the sample. A highly robust normal reference bandwidth is proposed, which adapts to different types of densities.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:stanee:v:63:y:2009:i:1:p:13-23
    DOI: 10.1111/j.1467-9574.2008.00392.x
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    Citations

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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.
    2. R. N. Rattihalli & S. B. Patil, 2021. "Data Dependent Asymmetric Kernels for Estimating the Density Function," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 155-186, February.
    3. Ordás Criado, C. & Grether, J.-M., 2011. "Convergence in per capita CO2 emissions: A robust distributional approach," Resource and Energy Economics, Elsevier, vol. 33(3), pages 637-665, September.
    4. Mia Hubert & Irène Gijbels & Dina Vanpaemel, 2013. "Reducing the mean squared error of quantile-based estimators by smoothing," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 448-465, September.
    5. Jenny Farmer & Donald Jacobs, 2018. "High throughput nonparametric probability density estimation," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-29, May.
    6. 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.

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