  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
