Variable selection in robust regression models for longitudinal data
In this article, we consider variable selection in robust regression models for longitudinal data. We propose a penalized robust estimating equation to estimate the regression parameters and to select the important covariate variables simultaneously. Under some regularity conditions, we show the ora...
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| Veröffentlicht in: | Journal of multivariate analysis 2012-08, Vol.109, p.156-167 |
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| container_title | Journal of multivariate analysis |
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| creator | Fan, Yali Qin, Guoyou Zhu, Zhongyi |
| description | In this article, we consider variable selection in robust regression models for longitudinal data. We propose a penalized robust estimating equation to estimate the regression parameters and to select the important covariate variables simultaneously. Under some regularity conditions, we show the oracle properties of the proposed robust variable selection methods. A simulation study shows the robustness of the proposed methods against outliers. Moreover, it is found by the simulation study that incorporating the correlation structure into the procedure of variable selection will lead to better performance than ignoring the correlation structure for longitudinal data. In the end, the proposed methods are illustrated in the analysis of a real data set. |
| doi_str_mv | 10.1016/j.jmva.2012.03.007 |
| format | Article |
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In the end, the proposed methods are illustrated in the analysis of a real data set.</description><subject>Correlation analysis</subject><subject>Longitudinal data</subject><subject>Longitudinal data Penalized estimating equation Robust method Variable selection</subject><subject>Mathematical models</subject><subject>Parameter estimation</subject><subject>Penalized estimating equation</subject><subject>Regression analysis</subject><subject>Robust method</subject><subject>Simulation</subject><subject>Studies</subject><subject>Variable selection</subject><issn>0047-259X</issn><issn>1095-7243</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNp9kEtLxTAQhYMoeH38AVcF162TpI8U3IhvFNyouAvTdKopvc016b3gvzf1iksXZwaScw7Dx9gJh4wDL8_6rF9uMBPARQYyA6h22IJDXaSVyOUuWwDkVSqK-m2fHYTQA3BeVPmCXb2it9gMlAQayEzWjYkdE--adZgST--eQpgfl66lISSd88ngxnc7rVs74pC0OOER2-twCHT8uw_Zy8318-Vd-vh0e3958ZiaAsoprZXCvEYsm8Z0ppIgpWiFUsTrEoiKFmpCpIorWaiWq06Q6kxe52iUKqtGHrLTbe_Ku881hUn3bu3jFUFHClyCkKqILrF1Ge9C8NTplbdL9F_RNPtK3euZlp5paZA60oqhh23I04rMX4KIZuuIeqMlRqBxfkX9RCXaKBO1mj-LUvOy0h_TMradb9siMtpY8joYS6Oh1voIWbfO_nfMN4_ajPE</recordid><startdate>20120801</startdate><enddate>20120801</enddate><creator>Fan, Yali</creator><creator>Qin, Guoyou</creator><creator>Zhu, Zhongyi</creator><general>Elsevier Inc</general><general>Elsevier</general><general>Taylor & Francis LLC</general><scope>6I.</scope><scope>AAFTH</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope></search><sort><creationdate>20120801</creationdate><title>Variable selection in robust regression models for longitudinal data</title><author>Fan, Yali ; Qin, Guoyou ; Zhu, Zhongyi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c506t-988a49aa6bbcfc730332d288e1960ee5d09eaae718358d18f2e8fc494ac8867b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Correlation analysis</topic><topic>Longitudinal data</topic><topic>Longitudinal data Penalized estimating equation Robust method Variable selection</topic><topic>Mathematical models</topic><topic>Parameter estimation</topic><topic>Penalized estimating equation</topic><topic>Regression analysis</topic><topic>Robust method</topic><topic>Simulation</topic><topic>Studies</topic><topic>Variable selection</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fan, Yali</creatorcontrib><creatorcontrib>Qin, Guoyou</creatorcontrib><creatorcontrib>Zhu, Zhongyi</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><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>Fan, Yali</au><au>Qin, Guoyou</au><au>Zhu, Zhongyi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Variable selection in robust regression models for longitudinal data</atitle><jtitle>Journal of multivariate analysis</jtitle><date>2012-08-01</date><risdate>2012</risdate><volume>109</volume><spage>156</spage><epage>167</epage><pages>156-167</pages><issn>0047-259X</issn><eissn>1095-7243</eissn><coden>JMVAAI</coden><abstract>In this article, we consider variable selection in robust regression models for longitudinal data. 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| language | eng |
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| subjects | Correlation analysis Longitudinal data Longitudinal data Penalized estimating equation Robust method Variable selection Mathematical models Parameter estimation Penalized estimating equation Regression analysis Robust method Simulation Studies Variable selection |
| title | Variable selection in robust regression models for longitudinal data |
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