Estimating the mean of heavy tailed distribution under random truncation

BEN DAHMANE, Khanssa (2022) Estimating the mean of heavy tailed distribution under random truncation. Doctoral thesis, Université de mohamed kheider biskra.

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The main aim of this thesis is to deploy and develop a new estimator for the mean that is based on the famous paper by Peng, 2001. Our case focuses on dealing with data when it becomes incomplete with a particular interest in the case of right-truncated, an asymptotic estimator is proposed and its behavior examined in a simulation study. We treat throughout our study two branches: Survival Analysis and Extreme Value Theory which has emerged as one of the most important statistical disciplines for the applied sciences over the last 50 years. The �rst objective of this thesis is to collect and simplify what has been done in the study of extreme values theory. This branch is interested in rare events and the causes of all disasters we know and of all economic crises. In addition, the second objective is to present an introduction that is devoted to the basic notions of survival analysis. Furthermore, we present two cases of incomplete data (censored and truncated) with giving the non-parametric estimator of the mean for each case.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Asymptotically normality, Extreme value Theory, Extreme value index, Lynden-Bell estimator, Random variation, Heavy-tails, Random truncation
Subjects: Q Science > QA Mathematics
Divisions: Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie > Département de Mathématiques
Depositing User: BFSE
Date Deposited: 15 May 2022 15:03
Last Modified: 15 May 2022 15:03

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