A class of unbiased location invariant Hill-type estimators for heavy tailed distributions

A class of unbiased location invariant Hill-type estimators for heavy tailed distributions

Based on the methods provided in Caeiro and Gomes (2002) and Fraga Alves (2001), a new class of location invariant Hill-type estimators is derived in this paper. Its asymptotic distributional representation and asymptotic normality are presented, and the optimal choice of sample fraction by Mean Squ...

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Veröffentlicht in:arXiv.org 2008-09
Hauptverfasser: Li, Jiaona, Peng, Zuoxiang, Nadarajah, Saralees
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Asymptotic properties
Computer simulation
Economic models
Estimators
Invariants
Mathematics - Statistics Theory
Monte Carlo simulation
Normality
Statistics - Theory
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description Based on the methods provided in Caeiro and Gomes (2002) and Fraga Alves (2001), a new class of location invariant Hill-type estimators is derived in this paper. Its asymptotic distributional representation and asymptotic normality are presented, and the optimal choice of sample fraction by Mean Squared Error is also discussed for some special cases. Finally comparison studies are provided for some familiar models by Monte Carlo simulations.
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subjects Asymptotic properties
Computer simulation
Economic models
Estimators
Invariants
Mathematics - Statistics Theory
Monte Carlo simulation
Normality
Statistics - Theory
title A class of unbiased location invariant Hill-type estimators for heavy tailed distributions
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