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Objective Bayesian analysis for the Lomax distribution

Author

Listed:
  • Ferreira, Paulo H.
  • Ramos, Eduardo
  • Ramos, Pedro L.
  • Gonzales, Jhon F.B.
  • Tomazella, Vera L.D.
  • Ehlers, Ricardo S.
  • Silva, Eveliny B.
  • Louzada, Francisco

Abstract

In this paper, we propose to make Bayesian inferences for the parameters of the Lomax distribution using non-informative priors, namely the (dependent and independent) Jeffreys prior and the reference prior. We assess Bayesian estimation through a Monte Carlo study with 10,000 simulated datasets. In order to evaluate the possible impact of prior specification on estimation, two criteria were considered: the mean relative error and the mean square error. An application on a real dataset illustrates the developed procedures.

Suggested Citation

  • Ferreira, Paulo H. & Ramos, Eduardo & Ramos, Pedro L. & Gonzales, Jhon F.B. & Tomazella, Vera L.D. & Ehlers, Ricardo S. & Silva, Eveliny B. & Louzada, Francisco, 2020. "Objective Bayesian analysis for the Lomax distribution," Statistics & Probability Letters, Elsevier, vol. 159(C).
    • Handle: RePEc:eee:stapro:v:159:y:2020:i:c:s0167715219303232
    DOI: 10.1016/j.spl.2019.108677
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    References listed on IDEAS

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    1. Catalina A. Vallejos & Mark F. J. Steel, 2015. "Objective Bayesian Survival Analysis Using Shape Mixtures of Log-Normal Distributions," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 697-710, June.
    2. Howlader, Hatem A. & Hossain, Anwar M., 2002. "Bayesian survival estimation of Pareto distribution of the second kind based on failure-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 38(3), pages 301-314, January.
    3. Fu, Jiayu & Xu, Ancha & Tang, Yincai, 2012. "Objective Bayesian analysis of Pareto distribution under progressive Type-II censoring," Statistics & Probability Letters, Elsevier, vol. 82(10), pages 1829-1836.
    4. Wang, Haiying & Sun, Dongchu, 2012. "Objective Bayesian analysis for a truncated model," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2125-2135.
    5. Tanabe, Ryunosuke & Hamada, Etsuo, 2016. "Objective priors for the zero-modified model," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 92-97.
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