Comparison of asymptotically unbiased extreme value index estimators: a Monte Carlo simulation study

Frederico Almeida Gião Gonçalves Caeiro; M. Ivette Gomes

doi:10.1063/1.4897797

Comparison of asymptotically unbiased extreme value index estimators: a Monte Carlo simulation study

Frederico Almeida Gião Gonçalves Caeiro, M. Ivette Gomes

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

In this paper we are interested in the semi-parametric estimation of the extreme value index of a heavy-tailed model. We consider a class of consistent semi-parametric estimators, parameterized with two tuning parameters. Such parameters enables us to have an estimator with a null dominant component of asymptotic bias, and achieve a high efficiency comparatively to other classical estimators. After a brief review of the estimators under study, we provide a Monte Carlo simulation study of the estimators behaviour for finite sample sizes of some familiar models.

Original language	English
Title of host publication	AIP Conference Proceedings
Pages	551-554
Volume	1618
DOIs	https://doi.org/10.1063/1.4897797
Publication status	Published - 6 Oct 2014
Event	International Conference of Computational Methods in Sciences and Engineering (ICCMSE) - Duration: 1 Jan 2014 → …

Conference

Conference	International Conference of Computational Methods in Sciences and Engineering (ICCMSE)
Period	1/01/14 → …

Keywords

Asymptotic properties
Extreme value index
Heavy tails
Monte Carlo simulation
Statistics of extremes

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10.1063/1.4897797

Cite this

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title = "Comparison of asymptotically unbiased extreme value index estimators: a Monte Carlo simulation study",

abstract = "In this paper we are interested in the semi-parametric estimation of the extreme value index of a heavy-tailed model. We consider a class of consistent semi-parametric estimators, parameterized with two tuning parameters. Such parameters enables us to have an estimator with a null dominant component of asymptotic bias, and achieve a high efficiency comparatively to other classical estimators. After a brief review of the estimators under study, we provide a Monte Carlo simulation study of the estimators behaviour for finite sample sizes of some familiar models.",

keywords = "Extreme value index, Monte Carlo simulation, Asymptotic properties, Statistics of extremes, Heavy tails, Asymptotic properties, Extreme value index, Heavy tails, Monte Carlo simulation, Statistics of extremes",

author = "Caeiro, {Frederico Almeida Gi{\~a}o Gon{\c c}alves} and Gomes, {M. Ivette}",

year = "2014",

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doi = "10.1063/1.4897797",

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T1 - Comparison of asymptotically unbiased extreme value index estimators: a Monte Carlo simulation study

AU - Caeiro, Frederico Almeida Gião Gonçalves

AU - Gomes, M. Ivette

PY - 2014/10/6

Y1 - 2014/10/6

N2 - In this paper we are interested in the semi-parametric estimation of the extreme value index of a heavy-tailed model. We consider a class of consistent semi-parametric estimators, parameterized with two tuning parameters. Such parameters enables us to have an estimator with a null dominant component of asymptotic bias, and achieve a high efficiency comparatively to other classical estimators. After a brief review of the estimators under study, we provide a Monte Carlo simulation study of the estimators behaviour for finite sample sizes of some familiar models.

AB - In this paper we are interested in the semi-parametric estimation of the extreme value index of a heavy-tailed model. We consider a class of consistent semi-parametric estimators, parameterized with two tuning parameters. Such parameters enables us to have an estimator with a null dominant component of asymptotic bias, and achieve a high efficiency comparatively to other classical estimators. After a brief review of the estimators under study, we provide a Monte Carlo simulation study of the estimators behaviour for finite sample sizes of some familiar models.

KW - Extreme value index

KW - Monte Carlo simulation

KW - Asymptotic properties

KW - Statistics of extremes

KW - Heavy tails

KW - Asymptotic properties

KW - Extreme value index

KW - Heavy tails

KW - Monte Carlo simulation

KW - Statistics of extremes

U2 - 10.1063/1.4897797

DO - 10.1063/1.4897797

M3 - Conference contribution

SN - 978-0-7354-1255-2

VL - 1618

SP - 551

EP - 554

BT - AIP Conference Proceedings

T2 - International Conference of Computational Methods in Sciences and Engineering (ICCMSE)

Y2 - 1 January 2014

ER -

Comparison of asymptotically unbiased extreme value index estimators: a Monte Carlo simulation study

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Keywords

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