Classical and Bayesian Inference for a Progressive First-Failure Censored Left-Truncated Normal Distribution

Point and interval estimations are taken into account for a progressive first-failure censored left-truncated normal distribution in this paper. First, we derive the estimators parameters on of maximum likelihood principle. Subsequently, construct asymptotic confidence intervals based these estimates log-transformed using normality estimators. Meanwhile, bootstrap methods also proposed construction intervals. As Bayesian estimation, implement Lindley approximation method to determine under not only symmetric loss function but asymmetric functions. The importance sampling procedure is applied at same time, highest posterior density (HPD) credible established procedure. efficiencies classical statistical inference evaluated through numerous simulations. We conclude that Bayes given by Linex highly recommended HPD possesses narrowest length among Ultimately, introduce an authentic dataset describing tensile strength 50mm carbon fibers as illustrative sample.