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Multivariate quantile function models

Yuzhi Cai Orcid Logo

Statistica Sinica, Volume: 20, Issue: 2, Pages: 481 - 496

Swansea University Author: Yuzhi Cai Orcid Logo

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Abstract

Multivariate quantiles have been defined by a number of researchers and can be estimated by different methods. However, little work can be found in the literature about Bayesian estimation of joint quantiles of multivariate random variables. In this paper we present a multivariate quantile function...

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Published in: Statistica Sinica
ISSN: 1017-0405
Published: Taipei 2010
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URI: https://cronfa.swan.ac.uk/Record/cronfa6997
first_indexed 2013-07-23T11:57:00Z
last_indexed 2018-02-09T04:34:42Z
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spelling 2016-05-01T15:24:34.8984188 v2 6997 2012-01-31 Multivariate quantile function models eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2012-01-31 CBAE Multivariate quantiles have been defined by a number of researchers and can be estimated by different methods. However, little work can be found in the literature about Bayesian estimation of joint quantiles of multivariate random variables. In this paper we present a multivariate quantile function model and propose a Bayesian method to estimate the model parameters. The methodology developed here enables us to estimate the multivariate quantile surfaces and the joint probability without direct use of the joint probability distribution or density functions of the random variables of interest. Furthermore, simulation studies and applications of the methodology to bivariate economics data sets show that the method works well both theoretically and practically. Journal Article Statistica Sinica 20 2 481 496 Taipei 1017-0405 Bayesian method, τth quantile surface, τth quantile curve, 31 12 2010 2010-12-31 http://www3.stat.sinica.edu.tw/statistica/j20n2/J20N21/J20N21.html COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2016-05-01T15:24:34.8984188 2012-01-31T11:05:25.6830000 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1
title Multivariate quantile function models
spellingShingle Multivariate quantile function models
Yuzhi Cai
title_short Multivariate quantile function models
title_full Multivariate quantile function models
title_fullStr Multivariate quantile function models
title_full_unstemmed Multivariate quantile function models
title_sort Multivariate quantile function models
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Yuzhi Cai
format Journal article
container_title Statistica Sinica
container_volume 20
container_issue 2
container_start_page 481
publishDate 2010
institution Swansea University
issn 1017-0405
publisher Taipei
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
url http://www3.stat.sinica.edu.tw/statistica/j20n2/J20N21/J20N21.html
document_store_str 0
active_str 0
description Multivariate quantiles have been defined by a number of researchers and can be estimated by different methods. However, little work can be found in the literature about Bayesian estimation of joint quantiles of multivariate random variables. In this paper we present a multivariate quantile function model and propose a Bayesian method to estimate the model parameters. The methodology developed here enables us to estimate the multivariate quantile surfaces and the joint probability without direct use of the joint probability distribution or density functions of the random variables of interest. Furthermore, simulation studies and applications of the methodology to bivariate economics data sets show that the method works well both theoretically and practically.
published_date 2010-12-31T06:13:58Z
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score 10.9520235

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