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Boundary performance of the beta kernel estimators

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  • Shunpu Zhang
  • Rohana Karunamuni

Abstract

The beta kernel estimators are shown in Chen [S.X. Chen, Beta kernel estimators for density functions, Comput. Statist. Data Anal. 31 (1999), pp. 131–145] to be non-negative and have less severe boundary problems than the conventional kernel estimator. Numerical results in Chen [S.X. Chen, Beta kernel estimators for density functions, Comput. Statist. Data Anal. 31 (1999), pp. 131–145] further show that beta kernel estimators have better finite sample performance than some of the widely used boundary corrected estimators. However, our study finds that the numerical comparisons of Chen are confounded with the choice of the bandwidths and the quantities being compared. In this paper, we show that the performances of the beta kernel estimators are very similar to that of the reflection estimator, which does not have the boundary problem only for densities exhibiting a shoulder at the endpoints of the support. For densities not exhibiting a shoulder, we show that the beta kernel estimators have a serious boundary problem and their performances at the boundary are inferior to that of the well-known boundary kernel estimator.

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  • Shunpu Zhang & Rohana Karunamuni, 2010. "Boundary performance of the beta kernel estimators," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(1), pages 81-104.
    • Handle: RePEc:taf:gnstxx:v:22:y:2010:i:1:p:81-104
    DOI: 10.1080/10485250903124984
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    1. Marcelo Fernandes & Paulo Monteiro, 2005. "Central limit theorem for asymmetric kernel functionals," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(3), pages 425-442, September.
    2. Karunamuni, R.J. & Zhang, S., 2008. "Some improvements on a boundary corrected kernel density estimator," Statistics & Probability Letters, Elsevier, vol. 78(5), pages 499-507, April.
    3. Chen, Song Xi, 1999. "Beta kernel estimators for density functions," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 131-145, August.
    4. Lejeune, Michel & Sarda, Pascal, 1992. "Smooth estimators of distribution and density functions," Computational Statistics & Data Analysis, Elsevier, vol. 14(4), pages 457-471, November.
    5. Renault, Olivier & Scaillet, Olivier, 2004. "On the way to recovery: A nonparametric bias free estimation of recovery rate densities," Journal of Banking & Finance, Elsevier, vol. 28(12), pages 2915-2931, December.
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    Cited by:

    1. Ouimet, Frédéric & Tolosana-Delgado, Raimon, 2022. "Asymptotic properties of Dirichlet kernel density estimators," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    2. Gao, Wenwu & Wang, Jiecheng & Zhang, Ran, 2023. "Quasi-interpolation for multivariate density estimation on bounded domain," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 592-608.
    3. Chen, Rongda & Zhou, Hanxian & Jin, Chenglu & Zheng, Wei, 2019. "Modeling of recovery rate for a given default by non-parametric method," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    4. Zhang, Shunpu, 2010. "A note on the performance of the gamma kernel estimators at the boundary," Statistics & Probability Letters, Elsevier, vol. 80(7-8), pages 548-557, April.
    5. Pierre Lafaye de Micheaux & Frédéric Ouimet, 2021. "A Study of Seven Asymmetric Kernels for the Estimation of Cumulative Distribution Functions," Mathematics, MDPI, vol. 9(20), pages 1-35, October.
    6. Christian Macaro & Raquel Prado, 2014. "Spectral Decompositions of Multiple Time Series: A Bayesian Non-parametric Approach," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 105-129, January.
    7. Jiecheng Wang & Yantong Liu & Jincai Chang, 2022. "An Improved Model for Kernel Density Estimation Based on Quadtree and Quasi-Interpolation," Mathematics, MDPI, vol. 10(14), pages 1-15, July.
    8. Bertin, Karine & Genest, Christian & Klutchnikoff, Nicolas & Ouimet, Frédéric, 2023. "Minimax properties of Dirichlet kernel density estimators," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
    9. Gery Geenens, 2014. "Probit Transformation for Kernel Density Estimation on the Unit Interval," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 346-358, March.

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