A new weighted quantile regression
The objective of the study is to use quantile regression to estimate extreme value events. The exploration of extreme value events requires the use of heavy-tailed distributions to build a model which fits the data well. One needs to estimate high conditional quantiles of a random variable for extre...
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Online Access: | http://dx.doi.org/10.1080/23311835.2017.1357237 |
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doaj-e235575bca2a4c7e86fff5a72d4ea22c2020-11-24T21:34:58ZengTaylor & Francis GroupCogent Mathematics2331-18352017-01-014110.1080/23311835.2017.13572371357237A new weighted quantile regressionMei Ling Huang0Ramona Rat1Brock UniversityBrock UniversityThe objective of the study is to use quantile regression to estimate extreme value events. The exploration of extreme value events requires the use of heavy-tailed distributions to build a model which fits the data well. One needs to estimate high conditional quantiles of a random variable for extreme events. Quantile regression ultimately yields results which the alternative mean regression method has no hope to offer, leading to it being labeled as the more powerful method. In order to improve this approach even further, a weighted quantile regression method is introduced with a complete comparison to the unweighted method. The Monte Carlo simulations show good results for the proposed weighted method. Comparisons of the proposed method and existing methods are given. The paper also investigates two real-world examples of applications on extreme events using the proposed weighted method.http://dx.doi.org/10.1080/23311835.2017.1357237bivariate Pareto distribution Type IIconditional quantilekernel conditional density estimatorgeneralized Pareto distributionlinear programmingweighted loss function |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mei Ling Huang Ramona Rat |
spellingShingle |
Mei Ling Huang Ramona Rat A new weighted quantile regression Cogent Mathematics bivariate Pareto distribution Type II conditional quantile kernel conditional density estimator generalized Pareto distribution linear programming weighted loss function |
author_facet |
Mei Ling Huang Ramona Rat |
author_sort |
Mei Ling Huang |
title |
A new weighted quantile regression |
title_short |
A new weighted quantile regression |
title_full |
A new weighted quantile regression |
title_fullStr |
A new weighted quantile regression |
title_full_unstemmed |
A new weighted quantile regression |
title_sort |
new weighted quantile regression |
publisher |
Taylor & Francis Group |
series |
Cogent Mathematics |
issn |
2331-1835 |
publishDate |
2017-01-01 |
description |
The objective of the study is to use quantile regression to estimate extreme value events. The exploration of extreme value events requires the use of heavy-tailed distributions to build a model which fits the data well. One needs to estimate high conditional quantiles of a random variable for extreme events. Quantile regression ultimately yields results which the alternative mean regression method has no hope to offer, leading to it being labeled as the more powerful method. In order to improve this approach even further, a weighted quantile regression method is introduced with a complete comparison to the unweighted method. The Monte Carlo simulations show good results for the proposed weighted method. Comparisons of the proposed method and existing methods are given. The paper also investigates two real-world examples of applications on extreme events using the proposed weighted method. |
topic |
bivariate Pareto distribution Type II conditional quantile kernel conditional density estimator generalized Pareto distribution linear programming weighted loss function |
url |
http://dx.doi.org/10.1080/23311835.2017.1357237 |
work_keys_str_mv |
AT meilinghuang anewweightedquantileregression AT ramonarat anewweightedquantileregression AT meilinghuang newweightedquantileregression AT ramonarat newweightedquantileregression |
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1725947089997791232 |