Estimation of extremes for heavy-tailed and light-tailed distributions in the presence of random censoring

In this paper, the flexible semi-parametric model introduced in is considered for conducting tail inference of censored data. Both and censoring variables are supposed to belong family distributions, thus solutions modelling data which between Weibull-tail Pareto-tail behaviours proposed. Estimators parameters extreme quantiles defined without prior knowledge strength asymptotic normality results proved. Various combinations tails distributions covered, ranging from rather mild severe tail, i.e., when ultimate probability zero. Finite sample behaviour presented via some simulations an illustration on real also provided.