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Estimation of Non-Crossing Quantile Regression Curves

Yuzhi Cai Orcid Logo, Tao Jiang

Australian & New Zealand Journal of Statistics, Volume: 57, Issue: 1, Pages: 139 - 162

Swansea University Author: Yuzhi Cai Orcid Logo

DOI (Published version): 10.1111/anzs.12106

Abstract

Quantile regression methods have been widely used in many research areas in recentyears. However conventional estimation methods for quantile regression models do notguarantee that the estimated quantile curves will be non-crossing. While there are variousmethods in the literature to deal with this...

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Published in: Australian & New Zealand Journal of Statistics
Published: 2015
URI: https://cronfa.swan.ac.uk/Record/cronfa21658
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first_indexed 2015-05-23T02:05:04Z
last_indexed 2018-02-09T04:59:25Z
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spelling 2017-09-11T11:21:01.4718270 v2 21658 2015-05-22 Estimation of Non-Crossing Quantile Regression Curves eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2015-05-22 BAF Quantile regression methods have been widely used in many research areas in recentyears. However conventional estimation methods for quantile regression models do notguarantee that the estimated quantile curves will be non-crossing. While there are variousmethods in the literature to deal with this problem, many of these methods force themodel parameters to lie within a subset of the parameter space in order for the requiredmonotonicity to be satisfied. Note that different methods may use different subspaces of thespace of model parameters. This paper establishes a relationship between the monotonicityof the estimated conditional quantiles and the comonotonicity of the model parameters.We develope a novel quasi-Bayesian method for parameter estimation which can be usedto deal with both time series and independent statistical data. Simulation studies and anapplication to real financial returns show that the proposed method has the potential to bevery useful in practice. Journal Article Australian & New Zealand Journal of Statistics 57 1 139 162 asymmetric Laplace distribution; comonotonicity; quasi-Bayesian method 18 3 2015 2015-03-18 10.1111/anzs.12106 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2017-09-11T11:21:01.4718270 2015-05-22T13:58:35.0721839 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1 Tao Jiang 2 0021658-26062017131428.pdf monotone-quantile.pdf 2017-06-26T13:14:28.9070000 Output 1003174 application/pdf Accepted Manuscript true 2017-06-26T00:00:00.0000000 true eng
title Estimation of Non-Crossing Quantile Regression Curves
spellingShingle Estimation of Non-Crossing Quantile Regression Curves
Yuzhi Cai
title_short Estimation of Non-Crossing Quantile Regression Curves
title_full Estimation of Non-Crossing Quantile Regression Curves
title_fullStr Estimation of Non-Crossing Quantile Regression Curves
title_full_unstemmed Estimation of Non-Crossing Quantile Regression Curves
title_sort Estimation of Non-Crossing Quantile Regression Curves
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Yuzhi Cai
Tao Jiang
format Journal article
container_title Australian & New Zealand Journal of Statistics
container_volume 57
container_issue 1
container_start_page 139
publishDate 2015
institution Swansea University
doi_str_mv 10.1111/anzs.12106
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 1
active_str 0
description Quantile regression methods have been widely used in many research areas in recentyears. However conventional estimation methods for quantile regression models do notguarantee that the estimated quantile curves will be non-crossing. While there are variousmethods in the literature to deal with this problem, many of these methods force themodel parameters to lie within a subset of the parameter space in order for the requiredmonotonicity to be satisfied. Note that different methods may use different subspaces of thespace of model parameters. This paper establishes a relationship between the monotonicityof the estimated conditional quantiles and the comonotonicity of the model parameters.We develope a novel quasi-Bayesian method for parameter estimation which can be usedto deal with both time series and independent statistical data. Simulation studies and anapplication to real financial returns show that the proposed method has the potential to bevery useful in practice.
published_date 2015-03-18T03:25:43Z
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score 11.0127

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