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Nonparametric estimators for quantile density function under length-biased sampling

Author

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  • Mahboubeh Akbari
  • Majid Rezaei
  • Sarah Jomhoori
  • Vahid Fakoor

Abstract

In this article, the strong uniform consistency of two nonparametric estimators for the quantile density function is established under length-biased sampling. The rate of the strong approximation of the resulting processes of these estimators will be presented as well. A Monte Carlo simulation study is carried out to compare the proposed estimators with each other in terms of mean squared errors.

Suggested Citation

  • Mahboubeh Akbari & Majid Rezaei & Sarah Jomhoori & Vahid Fakoor, 2019. "Nonparametric estimators for quantile density function under length-biased sampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(19), pages 4918-4935, October.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:19:p:4918-4935
    DOI: 10.1080/03610926.2018.1549245
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    Cited by:

    1. Yogendra P. Chaubey & Isha Dewan & Jun Li, 2021. "On Some Smooth Estimators of the Quantile Function for a Stationary Associated Process," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 114-139, May.

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