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Nonparametric estimation of hazard quantile function

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  • P. Sankaran
  • N. Unnikrishnan Nair

Abstract

In this paper, we study the estimation of the hazard quantile function based on right censored data. Two nonparametric estimators, one based on the empirical quantile density function and the other using the kernel smoothing method, are proposed. Asymptotic properties of the kernel-based estimator are discussed. Monte Carlo simulation studies are conducted to compare the two estimators. The method is illustrated for a real data set.

Suggested Citation

  • P. Sankaran & N. Unnikrishnan Nair, 2009. "Nonparametric estimation of hazard quantile function," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(6), pages 757-767.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:6:p:757-767
    DOI: 10.1080/10485250902919046
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    References listed on IDEAS

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    1. Cheng, Cheng, 2002. "Almost-sure uniform error bounds of general smooth estimators of quantile density functions," Statistics & Probability Letters, Elsevier, vol. 59(2), pages 183-194, September.
    2. M. Jones, 1992. "Estimating densities, quantiles, quantile densities and density quantiles," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(4), pages 721-727, December.
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    Cited by:

    1. P. Sankaran & Bijamma Thomas & N. Midhu, 2015. "On bilinear hazard quantile functions," METRON, Springer;Sapienza Università di Roma, vol. 73(1), pages 135-148, April.
    2. Sunoj, S.M. & Sankaran, P.G. & Nanda, Asok K., 2013. "Quantile based entropy function in past lifetime," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 366-372.
    3. P.G. Sankaran & N.N. Midhu, 2017. "Nonparametric estimation of mean residual quantile function under right censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1856-1874, July.
    4. Nanda, Asok K. & Sankaran, P.G. & Sunoj, S.M., 2014. "Rényi’s residual entropy: A quantile approach," Statistics & Probability Letters, Elsevier, vol. 85(C), pages 114-121.
    5. Soni, Pooja & Dewan, Isha & Jain, Kanchan, 2012. "Nonparametric estimation of quantile density function," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3876-3886.
    6. Sunoj, S.M. & Sankaran, P.G., 2012. "Quantile based entropy function," Statistics & Probability Letters, Elsevier, vol. 82(6), pages 1049-1053.
    7. Chesneau, Christophe & Dewan, Isha & Doosti, Hassan, 2016. "Nonparametric estimation of a quantile density function by wavelet methods," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 161-174.
    8. Sankaran, P.G. & Sunoj, S.M. & Nair, N. Unnikrishnan, 2016. "Kullback–Leibler divergence: A quantile approach," Statistics & Probability Letters, Elsevier, vol. 111(C), pages 72-79.
    9. Unnikrishnan Nair, N. & Vineshkumar, B., 2011. "Ageing concepts: An approach based on quantile function," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 2016-2025.
    10. Sankaran, P.G. & Unnikrishnan Nair, N. & Sreedevi, E.P., 2010. "A quantile based test for comparing cumulative incidence functions of competing risks models," Statistics & Probability Letters, Elsevier, vol. 80(9-10), pages 886-891, May.

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