No Cover Image

Journal article 1383 views

Bayesian nonparametric quantile regression using splines

Paul Thompson, Yuzhi Cai Orcid Logo, Rana Moyeed, Dominic Reeve Orcid Logo, Julian Stander

Computational Statistics and Data Analysis, Volume: 54, Issue: 4, Pages: 1138 - 1150

Swansea University Authors: Yuzhi Cai Orcid Logo, Dominic Reeve Orcid Logo

Full text not available from this repository: check for access using links below.

Check full text

DOI (Published version): 10.1016/j.csda.2009.09.004

Abstract

A new technique based on Bayesian quantile regression that models the dependence of a quantile of one variable on the values of another using a natural cubic spline is presented. Inference is based on the posterior density of the spline and an associated smoothing parameter and is performed by means...

Full description

Published in: Computational Statistics and Data Analysis
ISSN: 0167-9473
Published: Elsevier 2010
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa7005
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2013-07-23T11:57:02Z
last_indexed 2018-02-09T04:34:43Z
id cronfa7005
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2016-08-01T10:46:29.1311988</datestamp><bib-version>v2</bib-version><id>7005</id><entry>2012-02-01</entry><title>Bayesian nonparametric quantile regression using splines</title><swanseaauthors><author><sid>eff7b8626ab4cc6428eef52516fda7d6</sid><ORCID>0000-0003-3509-9787</ORCID><firstname>Yuzhi</firstname><surname>Cai</surname><name>Yuzhi Cai</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>3e76fcc2bb3cde4ddee2c8edfd2f0082</sid><ORCID>0000-0003-1293-4743</ORCID><firstname>Dominic</firstname><surname>Reeve</surname><name>Dominic Reeve</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2012-02-01</date><deptcode>BAF</deptcode><abstract>A new technique based on Bayesian quantile regression that models the dependence of a quantile of one variable on the values of another using a natural cubic spline is presented. Inference is based on the posterior density of the spline and an associated smoothing parameter and is performed by means of a Markov chain Monte Carlo algorithm. Examples of the application of the new technique to two real environmental data sets and to simulated data for which polynomial modelling is inappropriate are given. An aid for making a good choice of proposal density in the MetropolisHastings algorithm is discussed. The new nonparametric methodology provides more flexible modelling than the currently used Bayesian parametric quantile regression approach.</abstract><type>Journal Article</type><journal>Computational Statistics and Data Analysis</journal><volume>54</volume><journalNumber>4</journalNumber><paginationStart>1138</paginationStart><paginationEnd>1150</paginationEnd><publisher>Elsevier</publisher><issnPrint>0167-9473</issnPrint><keywords>non-parametric method, quantile regression, natural cubic splines, Bayesian method</keywords><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2010</publishedYear><publishedDate>2010-12-31</publishedDate><doi>10.1016/j.csda.2009.09.004</doi><url/><notes/><college>COLLEGE NANME</college><department>Accounting and Finance</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BAF</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2016-08-01T10:46:29.1311988</lastEdited><Created>2012-02-01T09:44:48.9770000</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Accounting and Finance</level></path><authors><author><firstname>Paul</firstname><surname>Thompson</surname><order>1</order></author><author><firstname>Yuzhi</firstname><surname>Cai</surname><orcid>0000-0003-3509-9787</orcid><order>2</order></author><author><firstname>Rana</firstname><surname>Moyeed</surname><order>3</order></author><author><firstname>Dominic</firstname><surname>Reeve</surname><orcid>0000-0003-1293-4743</orcid><order>4</order></author><author><firstname>Julian</firstname><surname>Stander</surname><order>5</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2016-08-01T10:46:29.1311988 v2 7005 2012-02-01 Bayesian nonparametric quantile regression using splines eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 3e76fcc2bb3cde4ddee2c8edfd2f0082 0000-0003-1293-4743 Dominic Reeve Dominic Reeve true false 2012-02-01 BAF A new technique based on Bayesian quantile regression that models the dependence of a quantile of one variable on the values of another using a natural cubic spline is presented. Inference is based on the posterior density of the spline and an associated smoothing parameter and is performed by means of a Markov chain Monte Carlo algorithm. Examples of the application of the new technique to two real environmental data sets and to simulated data for which polynomial modelling is inappropriate are given. An aid for making a good choice of proposal density in the MetropolisHastings algorithm is discussed. The new nonparametric methodology provides more flexible modelling than the currently used Bayesian parametric quantile regression approach. Journal Article Computational Statistics and Data Analysis 54 4 1138 1150 Elsevier 0167-9473 non-parametric method, quantile regression, natural cubic splines, Bayesian method 31 12 2010 2010-12-31 10.1016/j.csda.2009.09.004 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2016-08-01T10:46:29.1311988 2012-02-01T09:44:48.9770000 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Paul Thompson 1 Yuzhi Cai 0000-0003-3509-9787 2 Rana Moyeed 3 Dominic Reeve 0000-0003-1293-4743 4 Julian Stander 5
title Bayesian nonparametric quantile regression using splines
spellingShingle Bayesian nonparametric quantile regression using splines
Yuzhi Cai
Dominic Reeve
title_short Bayesian nonparametric quantile regression using splines
title_full Bayesian nonparametric quantile regression using splines
title_fullStr Bayesian nonparametric quantile regression using splines
title_full_unstemmed Bayesian nonparametric quantile regression using splines
title_sort Bayesian nonparametric quantile regression using splines
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
3e76fcc2bb3cde4ddee2c8edfd2f0082
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
3e76fcc2bb3cde4ddee2c8edfd2f0082_***_Dominic Reeve
author Yuzhi Cai
Dominic Reeve
author2 Paul Thompson
Yuzhi Cai
Rana Moyeed
Dominic Reeve
Julian Stander
format Journal article
container_title Computational Statistics and Data Analysis
container_volume 54
container_issue 4
container_start_page 1138
publishDate 2010
institution Swansea University
issn 0167-9473
doi_str_mv 10.1016/j.csda.2009.09.004
publisher Elsevier
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 0
active_str 0
description A new technique based on Bayesian quantile regression that models the dependence of a quantile of one variable on the values of another using a natural cubic spline is presented. Inference is based on the posterior density of the spline and an associated smoothing parameter and is performed by means of a Markov chain Monte Carlo algorithm. Examples of the application of the new technique to two real environmental data sets and to simulated data for which polynomial modelling is inappropriate are given. An aid for making a good choice of proposal density in the MetropolisHastings algorithm is discussed. The new nonparametric methodology provides more flexible modelling than the currently used Bayesian parametric quantile regression approach.
published_date 2010-12-31T03:08:39Z
_version_ 1763749835999019008
score 10.999161

Similar Items