Robust estimation of the Pickands dependence function under random right censoring

We consider bias-reduced estimation of the extreme value index in conditional Pareto-type models with random covariates when the response variable is subject to random right

Our aim in this section is to illustrate the efficiency of the proposed robust estimator for the conditional Pickands dependence function with a simulation study.. For this

We consider the estimation of the bivariate stable tail dependence function and propose a bias-corrected and robust estimator.. We establish its asymptotic behavior under

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

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

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

Maximum a posteriori and mean posterior estimators are constructed for various prior distributions of the tail index and their consistency and asymptotic normality are

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