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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…
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…
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…