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Generalized least squares cross-validation in kernel density estimation

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  • Jin Zhang

Abstract

type="main" xml:id="stan12061-abs-0001"> The kernel density estimation is a popular method in density estimation. The main issue is bandwidth selection, which is a well-known topic and is still frustrating statisticians. A robust least squares cross-validation bandwidth is proposed, which significantly improves the classical least squares cross-validation bandwidth for its variability and undersmoothing, adapts to different kinds of densities, and outperforms the existing bandwidths in statistical literature and software.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:stanee:v:69:y:2015:i:3:p:315-328
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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.
    2. Cao, R., 1993. "Bootstrapping the Mean Integrated Squared Error," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 137-160, April.
    3. 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.
    4. Cao, Ricardo & Cuevas, Antonio & Gonzalez Manteiga, Wensceslao, 1994. "A comparative study of several smoothing methods in density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 17(2), pages 153-176, February.
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    Cited by:

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    2. Alghalith, Moawia, 2016. "Novel and simple non-parametric methods of estimating the joint and marginal densities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 94-98.

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