A local moment type estimator for the extreme value index in regression with random covariates

The methodologies introduced in [Worms (2014)], in the heavy-tailed case, are adapted here to the negative extreme value index framework, leading to the definition of weighted

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

Estimating the conditional tail index with an integrated conditional log-quantile estimator in the random..

This is in line with Figure 1(b) of [11], which does not consider any covariate information and gives a value of this extreme survival time between 15 and 19 years while using

When analyzing the extremes of a random variable, a central issue is that the straightforward empirical estimator of the quantile function is not consistent at extreme levels; in

In this paper, we address estimation of the extreme-value index and extreme quantiles of a heavy-tailed distribution when some random covariate information is available and the data

From the Table 2, the moment and UH estimators of the conditional extreme quantile appear to be biased, even when the sample size is large, and much less robust to censoring than