23International audienceIn this paper, we investigate the estimation of the tail index and extreme quantiles of a heavy-tailed distribution when some covariate information is available and the data are randomly right-censored. We construct several estimators by combining a moving-window technique (for tackling the covariate information) and the inverse probability-of-censoring weighting method, and we establish their asymptotic normality. A comprehensive simulation study is conducted to evaluate the finite-sample performance of the proposed estimators and to identify their application scope

International audienceEstimation of the extreme-value index of a heavy-tailed distribution is addressed when some random covariate information is available and the data are randomly right-censored. An inverse-probability-of-censoring-weighted kernel version of Hill's estimator of the extreme-value index is proposed and its asymptotic normality is established. Based on this, a Weissman-type estimator of conditional extreme quantiles is also constructed. A simulation study is conducted to assess the finite-sample behaviour of the proposed estimators