Bayesian Estimation of the Transmuted Frechet Distribution

Transmuted distributions are a flexible skewed family of distributions and are currently used as lifetime models in reliability analysis. In this article, we consider transmuted Frechet distribution for Bayesian analysis. In particular, we use and compare non-informative and informative priors under squared error loss function, precautionary loss function and quadratic loss function to estimate the scale and shape parameters of the said distribution. A simulation study is conducted through the Markov Chain Monte Carlo algorithm for uncensored and censored environments in terms of different sample sizes and censoring rates. Moreover, an application is also presented to a real data set to investigate the flexibility and potentiality of the aforementioned distribution.