High quantile regression for extreme events

Abstract For extreme events, estimation of high conditional quantiles for heavy tailed distributions is an important problem. Quantile regression is a useful method in this field with many applications. Quantile regression uses an L 1-loss function, and an optimal solution by means of linear program...

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Main Authors: Mei Ling Huang, Christine Nguyen
Format: Article
Language:English
Published: SpringerOpen 2017-05-01
Series:Journal of Statistical Distributions and Applications
Subjects:
Online Access:http://link.springer.com/article/10.1186/s40488-017-0058-3
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spelling doaj-077553e9c2bb440e81a2c2be620279192020-11-25T00:21:26ZengSpringerOpenJournal of Statistical Distributions and Applications2195-58322017-05-014112010.1186/s40488-017-0058-3High quantile regression for extreme eventsMei Ling Huang0Christine Nguyen1Department of Mathematics & Statistics, Brock UniversityDepartment of Mathematics & Statistics, Brock UniversityAbstract For extreme events, estimation of high conditional quantiles for heavy tailed distributions is an important problem. Quantile regression is a useful method in this field with many applications. Quantile regression uses an L 1-loss function, and an optimal solution by means of linear programming. In this paper, we propose a weighted quantile regression method. Monte Carlo simulations are performed to compare the proposed method with existing methods for estimating high conditional quantiles. We also investigate two real-world examples by using the proposed weighted method. The Monte Carlo simulation and two real-world examples show the proposed method is an improvement of the existing method.http://link.springer.com/article/10.1186/s40488-017-0058-3Bivariate Pareto distributionConditional quantileExtreme value distributionGeneralized Pareto distributionLinear programmingWeighted loss function
collection DOAJ
language English
format Article
sources DOAJ
author Mei Ling Huang
Christine Nguyen
spellingShingle Mei Ling Huang
Christine Nguyen
High quantile regression for extreme events
Journal of Statistical Distributions and Applications
Bivariate Pareto distribution
Conditional quantile
Extreme value distribution
Generalized Pareto distribution
Linear programming
Weighted loss function
author_facet Mei Ling Huang
Christine Nguyen
author_sort Mei Ling Huang
title High quantile regression for extreme events
title_short High quantile regression for extreme events
title_full High quantile regression for extreme events
title_fullStr High quantile regression for extreme events
title_full_unstemmed High quantile regression for extreme events
title_sort high quantile regression for extreme events
publisher SpringerOpen
series Journal of Statistical Distributions and Applications
issn 2195-5832
publishDate 2017-05-01
description Abstract For extreme events, estimation of high conditional quantiles for heavy tailed distributions is an important problem. Quantile regression is a useful method in this field with many applications. Quantile regression uses an L 1-loss function, and an optimal solution by means of linear programming. In this paper, we propose a weighted quantile regression method. Monte Carlo simulations are performed to compare the proposed method with existing methods for estimating high conditional quantiles. We also investigate two real-world examples by using the proposed weighted method. The Monte Carlo simulation and two real-world examples show the proposed method is an improvement of the existing method.
topic Bivariate Pareto distribution
Conditional quantile
Extreme value distribution
Generalized Pareto distribution
Linear programming
Weighted loss function
url http://link.springer.com/article/10.1186/s40488-017-0058-3
work_keys_str_mv AT meilinghuang highquantileregressionforextremeevents
AT christinenguyen highquantileregressionforextremeevents
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