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

Transmuted distributions are flexible skewed families constructed by the induction of one or more additional parameters to a parent distribution. This paper investigates the potential usefulness of a two-component mixture of Transmuted Pareto Distribution (TPaD) under a Bayesian framework assuming type-I right censored sampling. For Bayesian analysis, noninformative as well as informative priors are assumed while three loss functions, namely the squared error loss function (SELF), precautionary loss function (PLF), and quadratic loss function (QLF) are considered to estimate the unknown parameters. Furthermore, Bayesian credible intervals (BCIs) are also discussed in this study. Since the posterior distributions do not have explicit forms, posterior summaries are computed using a Markov Chain Monte Carlo (MCMC) technique. The performance of the Bayes estimators is assessed by their posterior risks assuming different sample sizes and censoring rates. To highlight the practical significance of a twocomponent mixture of transmuted Pareto distribution (TPaD), a medical data set collected for rental calculi problem is analyzed in this study. Furthermore, annual flood rate data collected at the Floyd River are also discussed in this study. (c) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.