Least product relative error estimation
A least product relative error criterion is proposed for multiplicative regression models. It is invariant under scale transformation of the outcome and covariates. In addition, the objective function is smooth and convex, resulting in a simple and uniquely defined estimator of the regression parame...
Gespeichert in:
| Veröffentlicht in: | Journal of multivariate analysis 2016-02, Vol.144, p.91-98 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 98 |
|---|---|
| container_issue | |
| container_start_page | 91 |
| container_title | Journal of multivariate analysis |
| container_volume | 144 |
| creator | Chen, Kani Lin, Yuanyuan Wang, Zhanfeng Ying, Zhiliang |
| description | A least product relative error criterion is proposed for multiplicative regression models. It is invariant under scale transformation of the outcome and covariates. In addition, the objective function is smooth and convex, resulting in a simple and uniquely defined estimator of the regression parameter. It is shown that the estimator is asymptotically normal and that the simple plug-in variance estimation is valid. Simulation results confirm that the proposed method performs well. An application to body fat calculation is presented to illustrate the new method. |
| doi_str_mv | 10.1016/j.jmva.2015.10.017 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1752271564</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0047259X15002699</els_id><sourcerecordid>3907269121</sourcerecordid><originalsourceid>FETCH-LOGICAL-c372t-8461e67a2fbabca12524df9590d3f562affa40463895527389930b4231d8b5733</originalsourceid><addsrcrecordid>eNp9kEFLxDAQhYMouK7-AU8FD55aM5OkacGLLK4KC14UvIU0nUDLbrsm3QX_vSnr2dODx7x5j4-xW-AFcCgf-qLfHW2BHFQyCg76jC2A1yrXKMU5W3AudY6q_rpkVzH2nAMoLRfsfkM2Ttk-jO3BTVmgrZ26I2UUwhgyilO3S8Y4XLMLb7eRbv50yT7Xzx-r13zz_vK2etrkTmic8kqWQKW26BvbOAuoULa-VjVvhVclWu-t5LIUVa0U6iS14I1EAW3VKC3Ekt2d_qZF34fUb_rxEIZUaUArRA2qlOkKT1cujDEG8mYf0tDwY4CbGYjpzQzEzEBmLwFJocdTiNL-Y0fBRNfR4KjtArnJtGP3X_wXrd1ncw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1752271564</pqid></control><display><type>article</type><title>Least product relative error estimation</title><source>Access via ScienceDirect (Elsevier)</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Chen, Kani ; Lin, Yuanyuan ; Wang, Zhanfeng ; Ying, Zhiliang</creator><creatorcontrib>Chen, Kani ; Lin, Yuanyuan ; Wang, Zhanfeng ; Ying, Zhiliang</creatorcontrib><description>A least product relative error criterion is proposed for multiplicative regression models. It is invariant under scale transformation of the outcome and covariates. In addition, the objective function is smooth and convex, resulting in a simple and uniquely defined estimator of the regression parameter. It is shown that the estimator is asymptotically normal and that the simple plug-in variance estimation is valid. Simulation results confirm that the proposed method performs well. An application to body fat calculation is presented to illustrate the new method.</description><identifier>ISSN: 0047-259X</identifier><identifier>EISSN: 1095-7243</identifier><identifier>DOI: 10.1016/j.jmva.2015.10.017</identifier><identifier>CODEN: JMVAAI</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Asymptotic methods ; Body fat ; Mathematical models ; Multiplicative regression model ; Parameter estimation ; Product form ; Regression analysis ; Relative error ; Scale invariance ; Studies ; Variance estimation</subject><ispartof>Journal of multivariate analysis, 2016-02, Vol.144, p.91-98</ispartof><rights>2015 Elsevier Inc.</rights><rights>Copyright Taylor & Francis Group Feb 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-8461e67a2fbabca12524df9590d3f562affa40463895527389930b4231d8b5733</citedby><cites>FETCH-LOGICAL-c372t-8461e67a2fbabca12524df9590d3f562affa40463895527389930b4231d8b5733</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jmva.2015.10.017$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Chen, Kani</creatorcontrib><creatorcontrib>Lin, Yuanyuan</creatorcontrib><creatorcontrib>Wang, Zhanfeng</creatorcontrib><creatorcontrib>Ying, Zhiliang</creatorcontrib><title>Least product relative error estimation</title><title>Journal of multivariate analysis</title><description>A least product relative error criterion is proposed for multiplicative regression models. It is invariant under scale transformation of the outcome and covariates. In addition, the objective function is smooth and convex, resulting in a simple and uniquely defined estimator of the regression parameter. It is shown that the estimator is asymptotically normal and that the simple plug-in variance estimation is valid. Simulation results confirm that the proposed method performs well. An application to body fat calculation is presented to illustrate the new method.</description><subject>Asymptotic methods</subject><subject>Body fat</subject><subject>Mathematical models</subject><subject>Multiplicative regression model</subject><subject>Parameter estimation</subject><subject>Product form</subject><subject>Regression analysis</subject><subject>Relative error</subject><subject>Scale invariance</subject><subject>Studies</subject><subject>Variance estimation</subject><issn>0047-259X</issn><issn>1095-7243</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kEFLxDAQhYMouK7-AU8FD55aM5OkacGLLK4KC14UvIU0nUDLbrsm3QX_vSnr2dODx7x5j4-xW-AFcCgf-qLfHW2BHFQyCg76jC2A1yrXKMU5W3AudY6q_rpkVzH2nAMoLRfsfkM2Ttk-jO3BTVmgrZ26I2UUwhgyilO3S8Y4XLMLb7eRbv50yT7Xzx-r13zz_vK2etrkTmic8kqWQKW26BvbOAuoULa-VjVvhVclWu-t5LIUVa0U6iS14I1EAW3VKC3Ekt2d_qZF34fUb_rxEIZUaUArRA2qlOkKT1cujDEG8mYf0tDwY4CbGYjpzQzEzEBmLwFJocdTiNL-Y0fBRNfR4KjtArnJtGP3X_wXrd1ncw</recordid><startdate>201602</startdate><enddate>201602</enddate><creator>Chen, Kani</creator><creator>Lin, Yuanyuan</creator><creator>Wang, Zhanfeng</creator><creator>Ying, Zhiliang</creator><general>Elsevier Inc</general><general>Taylor & Francis LLC</general><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope></search><sort><creationdate>201602</creationdate><title>Least product relative error estimation</title><author>Chen, Kani ; Lin, Yuanyuan ; Wang, Zhanfeng ; Ying, Zhiliang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-8461e67a2fbabca12524df9590d3f562affa40463895527389930b4231d8b5733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Asymptotic methods</topic><topic>Body fat</topic><topic>Mathematical models</topic><topic>Multiplicative regression model</topic><topic>Parameter estimation</topic><topic>Product form</topic><topic>Regression analysis</topic><topic>Relative error</topic><topic>Scale invariance</topic><topic>Studies</topic><topic>Variance estimation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Kani</creatorcontrib><creatorcontrib>Lin, Yuanyuan</creatorcontrib><creatorcontrib>Wang, Zhanfeng</creatorcontrib><creatorcontrib>Ying, Zhiliang</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><jtitle>Journal of multivariate analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Kani</au><au>Lin, Yuanyuan</au><au>Wang, Zhanfeng</au><au>Ying, Zhiliang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Least product relative error estimation</atitle><jtitle>Journal of multivariate analysis</jtitle><date>2016-02</date><risdate>2016</risdate><volume>144</volume><spage>91</spage><epage>98</epage><pages>91-98</pages><issn>0047-259X</issn><eissn>1095-7243</eissn><coden>JMVAAI</coden><abstract>A least product relative error criterion is proposed for multiplicative regression models. It is invariant under scale transformation of the outcome and covariates. In addition, the objective function is smooth and convex, resulting in a simple and uniquely defined estimator of the regression parameter. It is shown that the estimator is asymptotically normal and that the simple plug-in variance estimation is valid. Simulation results confirm that the proposed method performs well. An application to body fat calculation is presented to illustrate the new method.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.jmva.2015.10.017</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0047-259X |
| ispartof | Journal of multivariate analysis, 2016-02, Vol.144, p.91-98 |
| issn | 0047-259X 1095-7243 |
| language | eng |
| recordid | cdi_proquest_journals_1752271564 |
| source | Access via ScienceDirect (Elsevier); EZB-FREE-00999 freely available EZB journals |
| subjects | Asymptotic methods Body fat Mathematical models Multiplicative regression model Parameter estimation Product form Regression analysis Relative error Scale invariance Studies Variance estimation |
| title | Least product relative error estimation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-31T16%3A22%3A24IST&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=Least%20product%20relative%20error%20estimation&rft.jtitle=Journal%20of%20multivariate%20analysis&rft.au=Chen,%20Kani&rft.date=2016-02&rft.volume=144&rft.spage=91&rft.epage=98&rft.pages=91-98&rft.issn=0047-259X&rft.eissn=1095-7243&rft.coden=JMVAAI&rft_id=info:doi/10.1016/j.jmva.2015.10.017&rft_dat=%3Cproquest_cross%3E3907269121%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=1752271564&rft_id=info:pmid/&rft_els_id=S0047259X15002699&rfr_iscdi=true |