General Entropy with Bayes Techniques under Lindley and MCMC for Estimating the New Weibull–Pareto Parameters: Theory and Application

Censored data play a pivotal role in life testing experiments since they significantly reduce cost and time. Hence, this paper investigates the problem of statistical inference for system progressive first-failure censoring new Weibull–Pareto distribution. Maximum likelihood estimates parameters as well some lifetime indices such reliability, hazard rate functions, coefficient variation are derived. Lindley approximation Markov chain Monte Carlo technique applied to obtain Bayes relative two different loss functions: balanced linear exponential general entropy functions. The results estimate computed under consideration informative prior function. A real-life example "the survival times years group patients given chemotherapy treatment" is presented illustrate proposed methods. Finally, simulation study carried out determine performance maximum compare corresponding confidence intervals.