Bias-corrected estimation for conditional Pareto-type distributions with random right censoring

Keywords: Elliptical distribution; Extreme quantiles; Extreme value theory; Haezendonck-Goovaerts risk measures; Heavy-tailed distributions; L p

However, a number of these works assume that the observed data come from heavy-tailed distributions (for both the sample of interest and the censoring sample), while many

However, the results on the extreme value index, in this case, are stated with a restrictive assumption on the ultimate probability of non-censoring in the tail and there is no

Abstract This paper presents new approaches for the estimation of the ex- treme value index in the framework of randomly censored (from the right) samples, based on the ideas

In order to validate the assumption of underlying conditional Pareto-type distributions for the claim sizes, we constructed local Pareto quantile plots of claim sizes for which

(2011), who used a fixed number of extreme conditional quantile estimators to estimate the conditional tail index, for instance using the Hill (Hill, 1975) and Pickands (Pickands,

More precisely, we consider the situation where pY p1q , Y p2q q is right censored by pC p1q , C p2q q, also following a bivariate distribution with an extreme value copula, but

We propose a nonparametric robust estimator for the tail index of a conditional Pareto-type distribution in the presence of censoring and random covariates.. The censored