LQ-moments for statistical analysis of extreme events

Statistical analysis of extremes is conducted for predicting large return periods events. LQ-moments that are based on linear combinations are reviewed for characterizing the upper quantiles of distributions and larger events in data. The LQ-moments method is presented based on a new quick estimator...

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主要な著者: Shabri, Ani, Jemain, Abdul Aziz
フォーマット: 論文
言語:English
出版事項: JMASM, Inc. 2007
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オンライン・アクセス:http://eprints.utm.my/id/eprint/7643/1/Anishabri2007_LQMomentsForStatisticalAnalysis.pdf
http://eprints.utm.my/id/eprint/7643/
http://tbf.coe.wayne.edu/jmasm/vol6_no1.pdf
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spelling my.utm.76432010-06-01T15:54:13Z http://eprints.utm.my/id/eprint/7643/ LQ-moments for statistical analysis of extreme events Shabri, Ani Jemain, Abdul Aziz QA Mathematics Statistical analysis of extremes is conducted for predicting large return periods events. LQ-moments that are based on linear combinations are reviewed for characterizing the upper quantiles of distributions and larger events in data. The LQ-moments method is presented based on a new quick estimator using five points quantiles and the weighted kernel estimator to estimate the parameters of the generalized extreme value (GEV) distribution. Monte Carlo methods illustrate the performance of LQ-moments in fitting the GEV distribution to both GEV and non-GEV samples. The proposed estimators of the GEV distribution were compared with conventional L-moments and LQ-moments based on linear interpolation quantiles for various sample sizes and return periods. The results indicate that the new method has generally good performance and makes it an attractive option for estimating quantiles in the GEV distribution. JMASM, Inc. 2007-05-01 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/7643/1/Anishabri2007_LQMomentsForStatisticalAnalysis.pdf Shabri, Ani and Jemain, Abdul Aziz (2007) LQ-moments for statistical analysis of extreme events. Journal of Modern Applied Statistical Methods, 6 (1). pp. 228-238. http://tbf.coe.wayne.edu/jmasm/vol6_no1.pdf
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Shabri, Ani
Jemain, Abdul Aziz
LQ-moments for statistical analysis of extreme events
description Statistical analysis of extremes is conducted for predicting large return periods events. LQ-moments that are based on linear combinations are reviewed for characterizing the upper quantiles of distributions and larger events in data. The LQ-moments method is presented based on a new quick estimator using five points quantiles and the weighted kernel estimator to estimate the parameters of the generalized extreme value (GEV) distribution. Monte Carlo methods illustrate the performance of LQ-moments in fitting the GEV distribution to both GEV and non-GEV samples. The proposed estimators of the GEV distribution were compared with conventional L-moments and LQ-moments based on linear interpolation quantiles for various sample sizes and return periods. The results indicate that the new method has generally good performance and makes it an attractive option for estimating quantiles in the GEV distribution.
format Article
author Shabri, Ani
Jemain, Abdul Aziz
author_facet Shabri, Ani
Jemain, Abdul Aziz
author_sort Shabri, Ani
title LQ-moments for statistical analysis of extreme events
title_short LQ-moments for statistical analysis of extreme events
title_full LQ-moments for statistical analysis of extreme events
title_fullStr LQ-moments for statistical analysis of extreme events
title_full_unstemmed LQ-moments for statistical analysis of extreme events
title_sort lq-moments for statistical analysis of extreme events
publisher JMASM, Inc.
publishDate 2007
url http://eprints.utm.my/id/eprint/7643/1/Anishabri2007_LQMomentsForStatisticalAnalysis.pdf
http://eprints.utm.my/id/eprint/7643/
http://tbf.coe.wayne.edu/jmasm/vol6_no1.pdf
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