6 papers
Weighted Gaussian Approximations for Increments of the Uniform Empirical and Quantile Processes: Fixed-Endpoint Extensions to the Finite-Count Scale
Abdelhakim Necir
We establish weighted Gaussian approximations for the uniform empirical and quantile processes and for their increments ending at a fixed point t in (0,1). We first place the class…
Weighted and Truncated Tail Index Estimation under Random Censoring: A Unified Full-Range Framework
Abdelhakim Necir, Nour Elhouda Guesmia, Djamel Meraghni
Estimation of the extreme value index under right censoring is a fundamental problem in extreme value theory, with important applications in finance, insurance, and reliability. Cl…
Robust Tail Index Estimation under Random Censoring via Minimum Density Power Divergence
Nour Elhouda Guesmia, Abdelhakim Necir, Djamel Meraghni
We propose a robust estimator for the tail index of Pareto-type distributions under random right-censoring, constructed within the minimum density power divergence (MDPD) framework…
Weighted Estimation of the Tail Index under Right Censorship: A Unified Approach Based on Kaplan-Meier and Nelson-Aalen Integrals
Abdelhakim Necir, Nour Elhouda Guesmia, Djamel Meraghni
Kaplan-Meier and Nelson-Aalen integral estimators to the tail index of right-censored Pareto-type data traditionally rely on the assumption that the proportion p of upper uncensore…
Robust and Smooth Estimation of the Extreme Tail Index via Weighted Minimum Density Power Divergence
Saida Mancer, Abdelhakim Necir, Djamel Meraghni
By introducing a weight function into the density power divergence, we develop a new class of robust and smooth estimators for the tail index of Pareto-type distributions, offering…
Nelson-Aalen kernel estimator to the tail index of right censored Pareto-type data
Nour Elhouda Guesmia, Abdelhakim Necir, Djamel Meraghni
On the basis of Nelson-Aalen product-limit estimator of a randomly censored distribution function, we introduce a kernel estimator to the tail index of right-censored Pareto-like d…