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A Class of Distributions with Linear Hazard Quantile Function

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  • N.N. Midhu
  • P.G. Sankaran
  • N. Unnikrishnan Nair

Abstract

In this article, we introduce and study a class of distributions that has linear hazard quantile function. Various distributional properties and reliability characteristics of the class are studied. Some characterizations of the class of distributions are presented. The method of L-moments is employed to estimate parameters of the class of distributions. Finally, we apply the proposed class to a real data set.

Suggested Citation

  • N.N. Midhu & P.G. Sankaran & N. Unnikrishnan Nair, 2014. "A Class of Distributions with Linear Hazard Quantile Function," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(17), pages 3674-3689, September.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:17:p:3674-3689
    DOI: 10.1080/03610926.2012.705211
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    Cited by:

    1. Tapan Kumar Chakrabarty & Dreamlee Sharma, 2021. "A Generalization of the Quantile-Based Flattened Logistic Distribution," Annals of Data Science, Springer, vol. 8(3), pages 603-627, September.
    2. 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.
    3. Bijamma Thomas & N.N. Midhu & P.G. Sankaran, 2015. "A software reliability model using mean residual quantile function," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1442-1457, July.
    4. 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.
    5. P. Sankaran & N. Midhu, 2016. "Testing exponentiality using mean residual quantile function," Statistical Papers, Springer, vol. 57(1), pages 235-247, March.
    6. Perepolkin, Dmytro & Goodrich, Benjamin & Sahlin, Ullrika, 2021. "The tenets of indirect inference in Bayesian models," OSF Preprints enzgs, Center for Open Science.
    7. 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.

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