Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data

Detalhes bibliográficos
Autor(a) principal: Martins, Eduardo Sávio Passos Rodrigues
Data de Publicação: 2000
Outros Autores: Stedinger, Jery Russell
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
Texto Completo: http://www.repositorio.ufc.br/handle/riufc/59412
Resumo: The three-parameter generalized extreme-value (GEV) distribution has found wide application for describing annual floods, rainfall, wind speeds, wave heights, snow depths, and other maxima. Previous studies show that small-sample maximum-likelihood estimators (MLE) of parameters are unstable and recommend L moment estimators. More recent research shows that method of moments quantile estimators have for −0.25 < κ < 0.30 smaller root-mean-square error than L moments and MLEs. Examination of the behavior of MLEs in small samples demonstrates that absurd values of the GEV-shape parameter κ can be generated. Use of a Bayesian prior distribution to restrict κ values to a statistically/physically reasonable range in a generalized maximum likelihood (GML) analysis eliminates this problem. In our examples the GML estimator did substantially better than moment and L moment quantile estimators for − 0.4 ≤ κ ≤ 0.
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spelling Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic dataGeneralized maximum-likelihood generalized extreme-value quantile estimators for hydrologic dataInundaçõesCheiasParâmetrosThe three-parameter generalized extreme-value (GEV) distribution has found wide application for describing annual floods, rainfall, wind speeds, wave heights, snow depths, and other maxima. Previous studies show that small-sample maximum-likelihood estimators (MLE) of parameters are unstable and recommend L moment estimators. More recent research shows that method of moments quantile estimators have for −0.25 < κ < 0.30 smaller root-mean-square error than L moments and MLEs. Examination of the behavior of MLEs in small samples demonstrates that absurd values of the GEV-shape parameter κ can be generated. Use of a Bayesian prior distribution to restrict κ values to a statistically/physically reasonable range in a generalized maximum likelihood (GML) analysis eliminates this problem. In our examples the GML estimator did substantially better than moment and L moment quantile estimators for − 0.4 ≤ κ ≤ 0.Water Resources Research2021-07-09T11:22:25Z2021-07-09T11:22:25Z2000info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfMARTINS, Eduardo Savio Passos Rodrigues; STEDINGER, Jery Russell. Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resources Research, United States, v. 36, n.3, p. 737-744, 2000.1944-7973http://www.repositorio.ufc.br/handle/riufc/59412Martins, Eduardo Sávio Passos RodriguesStedinger, Jery Russellengreponame:Repositório Institucional da Universidade Federal do Ceará (UFC)instname:Universidade Federal do Ceará (UFC)instacron:UFCinfo:eu-repo/semantics/openAccess2022-11-29T16:51:04Zoai:repositorio.ufc.br:riufc/59412Repositório InstitucionalPUBhttp://www.repositorio.ufc.br/ri-oai/requestbu@ufc.br || repositorio@ufc.bropendoar:2024-09-11T18:48:02.624455Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)false
dc.title.none.fl_str_mv Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data
Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data
title Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data
spellingShingle Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data
Martins, Eduardo Sávio Passos Rodrigues
Inundações
Cheias
Parâmetros
title_short Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data
title_full Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data
title_fullStr Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data
title_full_unstemmed Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data
title_sort Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data
author Martins, Eduardo Sávio Passos Rodrigues
author_facet Martins, Eduardo Sávio Passos Rodrigues
Stedinger, Jery Russell
author_role author
author2 Stedinger, Jery Russell
author2_role author
dc.contributor.author.fl_str_mv Martins, Eduardo Sávio Passos Rodrigues
Stedinger, Jery Russell
dc.subject.por.fl_str_mv Inundações
Cheias
Parâmetros
topic Inundações
Cheias
Parâmetros
description The three-parameter generalized extreme-value (GEV) distribution has found wide application for describing annual floods, rainfall, wind speeds, wave heights, snow depths, and other maxima. Previous studies show that small-sample maximum-likelihood estimators (MLE) of parameters are unstable and recommend L moment estimators. More recent research shows that method of moments quantile estimators have for −0.25 < κ < 0.30 smaller root-mean-square error than L moments and MLEs. Examination of the behavior of MLEs in small samples demonstrates that absurd values of the GEV-shape parameter κ can be generated. Use of a Bayesian prior distribution to restrict κ values to a statistically/physically reasonable range in a generalized maximum likelihood (GML) analysis eliminates this problem. In our examples the GML estimator did substantially better than moment and L moment quantile estimators for − 0.4 ≤ κ ≤ 0.
publishDate 2000
dc.date.none.fl_str_mv 2000
2021-07-09T11:22:25Z
2021-07-09T11:22:25Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv MARTINS, Eduardo Savio Passos Rodrigues; STEDINGER, Jery Russell. Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resources Research, United States, v. 36, n.3, p. 737-744, 2000.
1944-7973
http://www.repositorio.ufc.br/handle/riufc/59412
identifier_str_mv MARTINS, Eduardo Savio Passos Rodrigues; STEDINGER, Jery Russell. Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resources Research, United States, v. 36, n.3, p. 737-744, 2000.
1944-7973
url http://www.repositorio.ufc.br/handle/riufc/59412
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Water Resources Research
publisher.none.fl_str_mv Water Resources Research
dc.source.none.fl_str_mv reponame:Repositório Institucional da Universidade Federal do Ceará (UFC)
instname:Universidade Federal do Ceará (UFC)
instacron:UFC
instname_str Universidade Federal do Ceará (UFC)
instacron_str UFC
institution UFC
reponame_str Repositório Institucional da Universidade Federal do Ceará (UFC)
collection Repositório Institucional da Universidade Federal do Ceará (UFC)
repository.name.fl_str_mv Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)
repository.mail.fl_str_mv bu@ufc.br || repositorio@ufc.br
_version_ 1813028951376789504

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