Almost sure convergence of a tail index estimator in the presence of censoring

In Beirlant and Guillou 1] an exponential regression model was introduced on the basis of scaled log-spacing between subsequent extreme order statistics from a Pareto-type distribution in the presence of censoring. From this representation, they derived an estimator for the Pareto index. In this note, we revisit this adaptation of the popular Hill 5] estimator for heavy-tailed distributions, generalizing the almost sure convergence of this estimator under very general conditions on N_r, the number of non-censored observations. To cite this article: E. Delafosse, A. Guillou, C. R. Acad. Sci. Paris, Ser. I 335 (2002) 375–380.