The Role of Bootstrap Methodologies in the Estimation of a Negative Extreme Value Index

Frederico Almeida Gião Gonçalves Caeiro

The Role of Bootstrap Methodologies in the Estimation of a Negative Extreme Value Index

DM - Departamento de Matemática

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

Abstract

In this note we deal with the estimation, under a semi-parametric framework, of a negative extreme value index, the primary parameter in statistics of extremes. We consider a recent class of generalized negative moment estimators of a negative extreme value index. Apart from the usual integer parameter k, related to the number of top order statistics involved in the estimation, the estimator depend on an extra real parameter θ, which makes it highly flexible and possibly second-order unbiased for a large variety of models. We are interested on the study of the bootstrap method in Gomes et al. (2013) for the adaptive choice of the parameters.

Original language	Unknown
Title of host publication	Proceedings 59th ISI World Statistics Congress
Pages	103-108
Publication status	Published - 1 Jan 2013
Event	59th World Statistics Congress - Duration: 1 Jan 2013 → …

Conference

Conference	59th World Statistics Congress
Period	1/01/13 → …

Cite this

@inproceedings{6c31bf6871f7443694325282e24df06e,

title = "The Role of Bootstrap Methodologies in the Estimation of a Negative Extreme Value Index",

abstract = "In this note we deal with the estimation, under a semi-parametric framework, of a negative extreme value index, the primary parameter in statistics of extremes. We consider a recent class of generalized negative moment estimators of a negative extreme value index. Apart from the usual integer parameter k, related to the number of top order statistics involved in the estimation, the estimator depend on an extra real parameter θ, which makes it highly flexible and possibly second-order unbiased for a large variety of models. We are interested on the study of the bootstrap method in Gomes et al. (2013) for the adaptive choice of the parameters.",

keywords = "statistics of extremes, semi-parametric estimation, Bootstrap methods",

author = "Caeiro, {Frederico Almeida Gi{\~a}o Gon{\c c}alves}",

note = "PDF carregado pelo autor: parece ser a vers{\~a}o final que est{\'a} dispon{\'i}vel on-line; null ; Conference date: 01-01-2013",

year = "2013",

month = jan,

day = "1",

language = "Unknown",

isbn = "978-90-73592-34-6",

pages = "103--108",

booktitle = "Proceedings 59th ISI World Statistics Congress",

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T1 - The Role of Bootstrap Methodologies in the Estimation of a Negative Extreme Value Index

AU - Caeiro, Frederico Almeida Gião Gonçalves

N1 - PDF carregado pelo autor: parece ser a versão final que está disponível on-line

PY - 2013/1/1

Y1 - 2013/1/1

N2 - In this note we deal with the estimation, under a semi-parametric framework, of a negative extreme value index, the primary parameter in statistics of extremes. We consider a recent class of generalized negative moment estimators of a negative extreme value index. Apart from the usual integer parameter k, related to the number of top order statistics involved in the estimation, the estimator depend on an extra real parameter θ, which makes it highly flexible and possibly second-order unbiased for a large variety of models. We are interested on the study of the bootstrap method in Gomes et al. (2013) for the adaptive choice of the parameters.

AB - In this note we deal with the estimation, under a semi-parametric framework, of a negative extreme value index, the primary parameter in statistics of extremes. We consider a recent class of generalized negative moment estimators of a negative extreme value index. Apart from the usual integer parameter k, related to the number of top order statistics involved in the estimation, the estimator depend on an extra real parameter θ, which makes it highly flexible and possibly second-order unbiased for a large variety of models. We are interested on the study of the bootstrap method in Gomes et al. (2013) for the adaptive choice of the parameters.

KW - statistics of extremes

KW - semi-parametric estimation

KW - Bootstrap methods

M3 - Conference contribution

SN - 978-90-73592-34-6

SP - 103

EP - 108

BT - Proceedings 59th ISI World Statistics Congress

Y2 - 1 January 2013

ER -