Mixture of transmuted Pareto distribution: Properties, applications and estimation under Bayesian framework

Aslam, Muhammad; Ali, Sajid; Yousaf, Rahila; Shah, Ismail

doi:10.1016/j.jfranklin.2019.11.042

Cited by 6 publications

(1 citation statement)

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“…Moreover, a same pattern has also been observed in the cases of real-life applications, which confirmed that the proposed MCMC algorithm is efficient to estimate the unknown parameters in the Bayesian framework. In future, one can extend the work using mixture of transmuted Weibull distribution (Aslam et al, 2020). Also, Bayesian analysis of record values using transmuted Weibull distribution may be considered.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Estimation of Transmuted Weibull Distribution under Different Loss Functions

Yousaf

Ali

Aslam

2020

JRSS

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In this article, we aim to estimate the parameters of the transmuted Weibull distribution (TWD) using Bayesian approach, as the Weibull distribution plays an important role in reliability engineering and life testing problems. Informative and non-informative priors under squared error loss function (SELF), precautionary loss function (PLF) and quadratic loss function (QLF) are assumed to estimate the scale, the shape and the transmuted parameter of the TWD. In addition to this, we also compute the Bayesian credible intervals (BCIs). To estimate parameters, we adopt Markov Chain Monte Carlo (MCMC) technique assuming uncensored and censored environments in terms of different sample sizes and censoring rates. The posterior risks, associated with each estimator are used to compare the performance of different estimators. Two real data sets are analyzed to illustrate the flexibility of the proposed distribution.

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Section: Discussionmentioning

confidence: 99%