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Kernel estimation of extropy function under length-biased sampling

Author

Listed:
  • Rajesh, Richu
  • G., Rajesh
  • Sunoj, S.M.

Abstract

In this article, we address the problem of nonparametric kernel estimators of extropy function under length-biased sampling. The large sample properties of the proposed estimators namely, the consistency and asymptotic normality are verified under suitable regularity conditions. In addition, the finite sample behaviour of the proposed estimators is investigated via a simulation study. Application to real data is also reported.

Suggested Citation

  • Rajesh, Richu & G., Rajesh & Sunoj, S.M., 2022. "Kernel estimation of extropy function under length-biased sampling," Statistics & Probability Letters, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:stapro:v:181:y:2022:i:c:s0167715221002522
    DOI: 10.1016/j.spl.2021.109290
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    References listed on IDEAS

    as
    1. Guoxin Qiu & Kai Jia, 2018. "Extropy estimators with applications in testing uniformity," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 182-196, January.
    2. M. I. Borrajo & W. González-Manteiga & M. D. Martínez-Miranda, 2017. "Bandwidth selection for kernel density estimation with length-biased data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(3), pages 636-668, July.
    3. Jales, Hugo & Ma, Jun & Yu, Zhengfei, 2017. "Optimal bandwidth selection for local linear estimation of discontinuity in density," Economics Letters, Elsevier, vol. 153(C), pages 23-27.
    4. Hadi Alizadeh Noughabi & Jalil Jarrahiferiz, 2019. "On the estimation of extropy," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 31(1), pages 88-99, January.
    Full references (including those not matched with items on IDEAS)

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