Classical and Bayesian Inference on Finite Mixture of Exponentiated Kumaraswamy Gompertz and Exponentiated Kumaraswamy Fréchet Distributions under Progressive Type II Censoring with Applications

A finite mixture of exponentiated Kumaraswamy Gompertz and Fréchet is developed discussed as a novel probability model. We study some useful structural properties the proposed To estimate model parameters under classical method, we use maximum likelihood estimation using progressive type II censoring scheme. Under Bayesian paradigm carried out with gamma priors censored samples squared error loss function. demonstrate efficiency based on progressively censoring, simulation out. Three actual data sets are used an example, demonstrating that suggested in new class fits better than existing models available literature.