New Reduced-bias Estimators of a Positive Extreme Value Index

Noting that the classical Hill estimator of a positive extreme value index (EVI) is the logarithm of the mean of order-0 of a set of certain statistics, a more general class of EVI-estimators based on the mean of order-p (MOP), p ⩾ 0, of such statistics was recently introduced. The asymptotic behavi...

Full description

Saved in:
Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2016-03, Vol.45 (3), p.833-862
Main Authors: Gomes, M. Ivette, Brilhante, M. Fátima, Pestana, Dinis
Format: Article
Language:English
Subjects:
Asymptotic properties
Bias estimation
Computer simulation
Econometrics
Estimators
Extreme values
Heavy tails
Mathematical analysis
Monte Carlo simulation
Optimization
Primary 62G32
Samples
Secondary 65C05
Semi-parametric reduced-bias estimation
Statistics
Statistics of extremes
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Noting that the classical Hill estimator of a positive extreme value index (EVI) is the logarithm of the mean of order-0 of a set of certain statistics, a more general class of EVI-estimators based on the mean of order-p (MOP), p ⩾ 0, of such statistics was recently introduced. The asymptotic behavior of the class of MOP EVI-estimators is reviewed, and compared to their reduced-bias MOP (RBMOP) and optimal RBMOP versions, which are suggested here and studied both asymptotically and for finite samples, through a large-scale simulation study. Applications to simulated datasets are also put forward.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2013.875567