Bayesian composite confidence interval for the tail index under randomly right-censored data

Abstract: Bayesian composite likelihood estimation of the tail index of a heavy-tailed distribution is addressed when data are randomly right-censored. Maximum a posteriori and mean posterior estimators are constructed under Jeffrey's prior distribution of the tail index. Based on asymptotic results, some confidence regions (CR) for the tail index are constructed using posterior distribution and log-posterior ratio statistic. The proposed confidence regions are investigated via Finite-sample simulations. Finally, the proposed confidence regions are outperformed through two real datasets