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Bayesian Estimation of a New Pareto-Type Distribution Based on Mixed Gibbs Sampling Algorithm

Author

Listed:
  • Fanqun Li

    (Institute of Statistics and Applied Mathematics, Anhui University of Finance & Economics, Bengbu 233000, China)

  • Shanran Wei

    (Institute of Statistics and Applied Mathematics, Anhui University of Finance & Economics, Bengbu 233000, China)

  • Mingtao Zhao

    (Institute of Statistics and Applied Mathematics, Anhui University of Finance & Economics, Bengbu 233000, China)

Abstract

In this paper, based on the mixed Gibbs sampling algorithm, a Bayesian estimation procedure is proposed for a new Pareto-type distribution in the case of complete and type II censored samples. Simulation studies show that the proposed method is consistently superior to the maximize likelihood estimation in the context of small samples. Also, an analysis of some real data is provided to test the Bayesian estimation.

Suggested Citation

  • Fanqun Li & Shanran Wei & Mingtao Zhao, 2023. "Bayesian Estimation of a New Pareto-Type Distribution Based on Mixed Gibbs Sampling Algorithm," Mathematics, MDPI, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:18-:d:1304559
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    References listed on IDEAS

    as
    1. Mohamed Ibrahim & M. Masoom Ali & Haitham M. Yousof, 2023. "The Discrete Analogue of the Weibull G Family: Properties, Different Applications, Bayesian and Non-Bayesian Estimation Methods," Annals of Data Science, Springer, vol. 10(4), pages 1069-1106, August.
    2. Ali Saadati Nik & Akbar Asgharzadeh & Saralees Nadarajah, 2019. "Comparisons of Methods of Estimation for a New Pareto-type Distribution," Statistica, Department of Statistics, University of Bologna, vol. 79(3), pages 291-319.
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