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Posterior and predictive inferences for Marshall Olkin bivariate Weibull distribution via Markov chain Monte Carlo methods

Author

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  • Rakesh Ranjan

    (Banaras Hindu University)

  • Vastoshpati Shastri

    (Government Arts & Science College)

Abstract

This paper deals with the well known bi-variate Weibull distribution developed by Marshall and Olkin. In the light of prior information, this paper derives the posterior distribution and performs Markov chain Monte Carlo methods to obtain posterior based inferences. This paper also checks the sensitivity of posterior estimates by changing the prior variances followed by Bayesian prediction using sample-based approaches. Numerical illustrations are provided for real as well as simulated data sets.

Suggested Citation

  • Rakesh Ranjan & Vastoshpati Shastri, 2019. "Posterior and predictive inferences for Marshall Olkin bivariate Weibull distribution via Markov chain Monte Carlo methods," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(6), pages 1535-1543, December.
    • Handle: RePEc:spr:ijsaem:v:10:y:2019:i:6:d:10.1007_s13198-019-00903-9
    DOI: 10.1007/s13198-019-00903-9
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    References listed on IDEAS

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    1. Kundu, Debasis & Gupta, Arjun K., 2013. "Bayes estimation for the Marshall–Olkin bivariate Weibull distribution," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 271-281.
    2. Kundu, Debasis & Dey, Arabin Kumar, 2009. "Estimating the parameters of the Marshall-Olkin bivariate Weibull distribution by EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 956-965, February.
    3. Nandi, Swagata & Dewan, Isha, 2010. "An EM algorithm for estimating the parameters of bivariate Weibull distribution under random censoring," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1559-1569, June.
    4. G. Heinrich & U. Jensen, 1995. "Parameter estimation for a bivariate lifetime distribution in reliability with multivariate extensions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 42(1), pages 49-65, December.
    5. Sarhan, Ammar M. & Balakrishnan, N., 2007. "A new class of bivariate distributions and its mixture," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1508-1527, August.
    6. Patra, Kaushik & Dey, Dipak K., 1999. "A multivariate mixture of Weibull distributions in reliability modeling," Statistics & Probability Letters, Elsevier, vol. 45(3), pages 225-235, November.
    7. Ranjan, Rakesh & Singh, Sonam & Upadhyay, Satyanshu K., 2015. "A Bayes analysis of a competing risk model based on gamma and exponential failures," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 35-44.
    8. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
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