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Choice of degree of Bernstein polynomial model

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  • Wang, Tao
  • Guan, Zhong

Abstract

Methods based on moments and modes for choosing Bernstein polynomial model degree are proposed. Simulation showed that the mean integrated square errors of the maximum approximate Bernstein likelihood estimates of density using degrees selected by the proposed methods and the change-point method are smaller than that of the kernel density but either closer to or smaller than that of the parametric maximum likelihood estimator.

Suggested Citation

  • Wang, Tao & Guan, Zhong, 2023. "Choice of degree of Bernstein polynomial model," Statistics & Probability Letters, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:stapro:v:200:y:2023:i:c:s0167715223000925
    DOI: 10.1016/j.spl.2023.109868
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    References listed on IDEAS

    as
    1. Zhong Guan, 2017. "Bernstein polynomial model for grouped continuous data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(4), pages 831-848, October.
    2. Zhong Guan, 2016. "Efficient and robust density estimation using Bernstein type polynomials," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 250-271, June.
    3. Chen, Song Xi, 1999. "Beta kernel estimators for density functions," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 131-145, August.
    4. M.C. Jones & D.A. Henderson, 2007. "Miscellanea Kernel-Type Density Estimation on the Unit Interval," Biometrika, Biometrika Trust, vol. 94(4), pages 977-984.
    Full references (including those not matched with items on IDEAS)

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