Robust estimation of the Pickands dependence function under random right censoring

Estimating the conditional extreme-value index under random right-censoring

In extreme value theory, the extreme-value index is a parameter that controls the behavior of a cumulative distribution function in its right tail. Estimating this parameter is thus the first step when tackling a number of problems related to extreme events. In this paper, we introduce an estimator of the extreme-value index in the presence of a random covariate when the response variable is ri...

A New Estimator for the Pickands Dependence Function

Cumulative distribution function estimation under interval censoring case 1

We consider projection methods for the estimation of cumulative distribution function under interval censoring, case 1. Such censored data also known as current status data, arise when the only information available on the variable of interest is whether it is greater or less than an observed random time. Two types of adaptive estimators are investigated. The first one is a two-step estimator b...

The quantile process under random censoring

In this paper we discuss the asymptotical properties of quantile processes under random censoring. In contrast to most work in this area we prove weak convergence of an appropriately standardized quantile process under the assumption that the quantile regression model is only linear in the region, where the process is investigated. Additionally, we also discuss properties of the quantile proces...

Hazard Estimation under Generalized Censoring

This paper focuses on the problem of the estimation of the cumulative hazard function of a distribution on a general complete separable metric space when the data points are subject to censoring by an arbitrary adapted random set. A problem involving observability of the estimator proposed in [8] and [9] is resolved and a functional central limit theorem is proven for the revised estimator. Sev...