Truncated Cauchy Power Weibull-G Class of Distributions: Bayesian and Non-Bayesian Inference Modelling for COVID-19 and Carbon Fiber Data

The Truncated Cauchy Power Weibull-G class is presented as a new family of distributions. Unique models for this are in paper. statistical aspects the explored, including expansion density function, moments, incomplete moments (IMOs), residual life and reversed functions, entropy. maximum likelihood (ML) Bayesian estimations developed based on Type-II censored sample. properties Bayes estimators parameters studied under different loss functions (squared error function LINEX function). To create Markov-chain Monte Carlo samples from posterior density, Metropolis–Hasting technique was used with density. Using non-informative informative priors, full simulation carried out. estimator compared to using simulation. compare performances suggested estimators, study Real-world data sets, such strength measured GPA single carbon fibers impregnated 1000-carbon fiber tows, stress per cycle at 31,000 psi, COVID-19 were demonstrate relevance flexibility method. then comparable Marshall–Olkin alpha power exponential, extended odd Weibull Weibull–Rayleigh, Weibull–Lomax, exponential Lomax