Estimation and Prediction for Flexible Weibull Distribution Based on Progressive Type II Censored Data

Abstract

In this work, we consider the problem of estimating the parameters and predicting the unobserved or removed ordered data for the progressive type II censored flexible Weibull sample. Frequentist and Bayesian analyses are adopted for conducting the estimation and prediction problems. The likelihood method as well as the Bayesian sampling techniques is applied for the inference problems. The point predictors and credible intervals of unobserved data based on an informative set of data are computed. Markov Chain Monte Carlo samples are performed to compare the so-obtained methods, and one real data set is analyzed for illustrative purposes.

Keywords

Flexible Weibull distribution / Progressive censoring data / Bayes estimation / Bayes prediction / Gibbs sampling / Simulation

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