Bayesian Estimation of Transmuted Pareto Distribution for Complete and Censored Data

Muhammad Aslam (), Rahila Yousaf () and Sajid Ali ()
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Muhammad Aslam: Riphah International University
Rahila Yousaf: Riphah International University
Sajid Ali: Quaid-i-Azam University

Annals of Data Science, 2020, vol. 7, issue 4, No 7, 663-695

Abstract: Abstract Transmuted distributions belong to the skewed family of distributions which are more flexible and versatile than the simple probability distributions. The focus of this article is the Bayesian estimation of three-parameter Transmuted Pareto distribution. In particular, we assumed noninformative and informative priors to obtain the posterior distributions. Bayesian point estimators and the associated precision measures are investigated under squared error loss function, precautionary loss function, and quadratic loss function. In addition to this, the Bayesian credible intervals are also computed under different priors. A simulation study using a Markov Chain Monte Carlo algorithm assuming uncensored and censored data in terms of different sample sizes and censoring rates is also a part of this study. The performance of Bayesian point estimators is assessed in term of posterior risks. Finally, two real life data sets of cardiovascular disease patients and of exceedances of Wheaton River flood are discussed in this article.

Keywords: Transmuted Pareto distribution; Loss functions; Bayes estimators; Posterior risks; Uniform prior; Informative prior; BCIs; MCMC; Censoring and Chi square test (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s40745-020-00310-z

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