On a multivariate regression model for rates and proportions
The paper by Johnson [Systems of frequency curves generated by the methods of translation, Biometrika 36 (2014), pp. 149-176] has introduced a very interesting univariate distribution with bounded support which is known in the statistical literature as the [Formula omitted.] class of distributions....
Gespeichert in:
Veröffentlicht in: | Journal of applied statistics 2019-04, Vol.46 (6), p.1084-1106 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 1106 |
---|---|
container_issue | 6 |
container_start_page | 1084 |
container_title | Journal of applied statistics |
container_volume | 46 |
creator | Lemonte, Artur J. Moreno–Arenas, Germán |
description | The paper by Johnson [Systems of frequency curves generated by the methods of translation, Biometrika 36 (2014), pp. 149-176] has introduced a very interesting univariate distribution with bounded support which is known in the statistical literature as the [Formula omitted.] class of distributions. In this paper we generalize this class of univariate distributions to the multivariate case whose marginals are [Formula omitted.] distributions. On the basis of the multivariate distribution introduced, we propose a multivariate regression model for dealing with multivariate response variables which are vectors of rates or proportions. We consider a frequentist approach to perform inferences, and the maximum likelihood method is employed to estimate the model parameters. Monte Carlo simulation results reveal that the maximum likelihood method can be used effectively in estimating the model parameters. An application to real data is presented to show the usefulness of the multivariate regression model in practice. |
doi_str_mv | 10.1080/02664763.2018.1534945 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2183936822</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2183936822</sourcerecordid><originalsourceid>FETCH-LOGICAL-c281t-c25fb127abb714b2f96912e978804bce42a3e508b4bdfbe1ff51c1a18ab20e873</originalsourceid><addsrcrecordid>eNo1kF1LwzAUhoMoOKc_QQh43XlOPtoUvJGhUxjsRq9D0ibS0TUzaQX_vSmbN-dcvA_vCw8h9wgrBAWPwMpSVCVfMUC1QslFLeQFWSAvoQDJ2SVZzEwxQ9fkJqU9AKgMLsjTbqCGHqZ-7H5M7MzoaHRf0aXUhYEeQut66kOkMSeJmqGlxxiOIY45Trfkyps-ubvzX5LP15eP9Vux3W3e18_bomEKx3ylt8gqY22FwjJflzUyV1dKgbCNE8xwJ0FZYVtvHXovsUGDylgGTlV8SR5OvXn7e3Jp1PswxSFPaoaK17xUjGVKnqgmhpSi8_oYu4OJvxpBz6L0vyg9i9JnUfwPIKdbEg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2183936822</pqid></control><display><type>article</type><title>On a multivariate regression model for rates and proportions</title><source>EBSCOhost Business Source Complete</source><creator>Lemonte, Artur J. ; Moreno–Arenas, Germán</creator><creatorcontrib>Lemonte, Artur J. ; Moreno–Arenas, Germán</creatorcontrib><description>The paper by Johnson [Systems of frequency curves generated by the methods of translation, Biometrika 36 (2014), pp. 149-176] has introduced a very interesting univariate distribution with bounded support which is known in the statistical literature as the [Formula omitted.] class of distributions. In this paper we generalize this class of univariate distributions to the multivariate case whose marginals are [Formula omitted.] distributions. On the basis of the multivariate distribution introduced, we propose a multivariate regression model for dealing with multivariate response variables which are vectors of rates or proportions. We consider a frequentist approach to perform inferences, and the maximum likelihood method is employed to estimate the model parameters. Monte Carlo simulation results reveal that the maximum likelihood method can be used effectively in estimating the model parameters. An application to real data is presented to show the usefulness of the multivariate regression model in practice.</description><identifier>ISSN: 0266-4763</identifier><identifier>EISSN: 1360-0532</identifier><identifier>DOI: 10.1080/02664763.2018.1534945</identifier><language>eng</language><publisher>Abingdon: Taylor & Francis Ltd</publisher><subject>Computer simulation ; Economic models ; Maximum likelihood method ; Monte Carlo simulation ; Parameter estimation ; Regression models ; Statistical analysis ; Statistical methods</subject><ispartof>Journal of applied statistics, 2019-04, Vol.46 (6), p.1084-1106</ispartof><rights>2018 Informa UK Limited, trading as Taylor & Francis Group</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c281t-c25fb127abb714b2f96912e978804bce42a3e508b4bdfbe1ff51c1a18ab20e873</citedby><cites>FETCH-LOGICAL-c281t-c25fb127abb714b2f96912e978804bce42a3e508b4bdfbe1ff51c1a18ab20e873</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Lemonte, Artur J.</creatorcontrib><creatorcontrib>Moreno–Arenas, Germán</creatorcontrib><title>On a multivariate regression model for rates and proportions</title><title>Journal of applied statistics</title><description>The paper by Johnson [Systems of frequency curves generated by the methods of translation, Biometrika 36 (2014), pp. 149-176] has introduced a very interesting univariate distribution with bounded support which is known in the statistical literature as the [Formula omitted.] class of distributions. In this paper we generalize this class of univariate distributions to the multivariate case whose marginals are [Formula omitted.] distributions. On the basis of the multivariate distribution introduced, we propose a multivariate regression model for dealing with multivariate response variables which are vectors of rates or proportions. We consider a frequentist approach to perform inferences, and the maximum likelihood method is employed to estimate the model parameters. Monte Carlo simulation results reveal that the maximum likelihood method can be used effectively in estimating the model parameters. An application to real data is presented to show the usefulness of the multivariate regression model in practice.</description><subject>Computer simulation</subject><subject>Economic models</subject><subject>Maximum likelihood method</subject><subject>Monte Carlo simulation</subject><subject>Parameter estimation</subject><subject>Regression models</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><issn>0266-4763</issn><issn>1360-0532</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNo1kF1LwzAUhoMoOKc_QQh43XlOPtoUvJGhUxjsRq9D0ibS0TUzaQX_vSmbN-dcvA_vCw8h9wgrBAWPwMpSVCVfMUC1QslFLeQFWSAvoQDJ2SVZzEwxQ9fkJqU9AKgMLsjTbqCGHqZ-7H5M7MzoaHRf0aXUhYEeQut66kOkMSeJmqGlxxiOIY45Trfkyps-ubvzX5LP15eP9Vux3W3e18_bomEKx3ylt8gqY22FwjJflzUyV1dKgbCNE8xwJ0FZYVtvHXovsUGDylgGTlV8SR5OvXn7e3Jp1PswxSFPaoaK17xUjGVKnqgmhpSi8_oYu4OJvxpBz6L0vyg9i9JnUfwPIKdbEg</recordid><startdate>20190426</startdate><enddate>20190426</enddate><creator>Lemonte, Artur J.</creator><creator>Moreno–Arenas, Germán</creator><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20190426</creationdate><title>On a multivariate regression model for rates and proportions</title><author>Lemonte, Artur J. ; Moreno–Arenas, Germán</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c281t-c25fb127abb714b2f96912e978804bce42a3e508b4bdfbe1ff51c1a18ab20e873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer simulation</topic><topic>Economic models</topic><topic>Maximum likelihood method</topic><topic>Monte Carlo simulation</topic><topic>Parameter estimation</topic><topic>Regression models</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lemonte, Artur J.</creatorcontrib><creatorcontrib>Moreno–Arenas, Germán</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of applied statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lemonte, Artur J.</au><au>Moreno–Arenas, Germán</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On a multivariate regression model for rates and proportions</atitle><jtitle>Journal of applied statistics</jtitle><date>2019-04-26</date><risdate>2019</risdate><volume>46</volume><issue>6</issue><spage>1084</spage><epage>1106</epage><pages>1084-1106</pages><issn>0266-4763</issn><eissn>1360-0532</eissn><abstract>The paper by Johnson [Systems of frequency curves generated by the methods of translation, Biometrika 36 (2014), pp. 149-176] has introduced a very interesting univariate distribution with bounded support which is known in the statistical literature as the [Formula omitted.] class of distributions. In this paper we generalize this class of univariate distributions to the multivariate case whose marginals are [Formula omitted.] distributions. On the basis of the multivariate distribution introduced, we propose a multivariate regression model for dealing with multivariate response variables which are vectors of rates or proportions. We consider a frequentist approach to perform inferences, and the maximum likelihood method is employed to estimate the model parameters. Monte Carlo simulation results reveal that the maximum likelihood method can be used effectively in estimating the model parameters. An application to real data is presented to show the usefulness of the multivariate regression model in practice.</abstract><cop>Abingdon</cop><pub>Taylor & Francis Ltd</pub><doi>10.1080/02664763.2018.1534945</doi><tpages>23</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0266-4763 |
ispartof | Journal of applied statistics, 2019-04, Vol.46 (6), p.1084-1106 |
issn | 0266-4763 1360-0532 |
language | eng |
recordid | cdi_proquest_journals_2183936822 |
source | EBSCOhost Business Source Complete |
subjects | Computer simulation Economic models Maximum likelihood method Monte Carlo simulation Parameter estimation Regression models Statistical analysis Statistical methods |
title | On a multivariate regression model for rates and proportions |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-20T18%3A40%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=On%20a%20multivariate%20regression%20model%20for%20rates%20and%20proportions&rft.jtitle=Journal%20of%20applied%20statistics&rft.au=Lemonte,%20Artur%20J.&rft.date=2019-04-26&rft.volume=46&rft.issue=6&rft.spage=1084&rft.epage=1106&rft.pages=1084-1106&rft.issn=0266-4763&rft.eissn=1360-0532&rft_id=info:doi/10.1080/02664763.2018.1534945&rft_dat=%3Cproquest_cross%3E2183936822%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2183936822&rft_id=info:pmid/&rfr_iscdi=true |