Statistical Inference on a Finite Mixture of Exponentiated Kumaraswamy-G Distributions with Progressive Type II Censoring Using Bladder Cancer Data

A new family of distributions called the mixture exponentiated Kumaraswamy-G (henceforth, in short, ExpKum-G) class is developed. We consider Weibull distribution as baseline (G) to propose and study this special sub-model, which we call Kumaraswamy distribution. Several useful statistical properties proposed ExpKum-G are derived. Under classical paradigm, maximum likelihood estimation under progressive type II censoring estimate model parameters. Bayesian independent gamma priors parameters censored samples, assuming several loss functions. simulation carried out illustrate efficiency strategies both paradigms, based on progressively models. For illustrative purposes, a real data set considered that exhibits provides better fit than other types finite mixtures Kumaraswamy-type