Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models
Considering model averaging estimation in generalized linear models, we propose a weight choice criterion based on the Kullback-Leibler (KL) loss with a penalty term. This criterion is different from that for continuous observations in principle, but reduces to the Mallows criterion in the situation...
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| Veröffentlicht in: | Journal of the American Statistical Association 2016-12, Vol.111 (516), p.1775-1790 |
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| container_issue | 516 |
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| container_title | Journal of the American Statistical Association |
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| creator | Zhang, Xinyu Yu, Dalei Zou, Guohua Liang, Hua |
| description | Considering model averaging estimation in generalized linear models, we propose a weight choice criterion based on the Kullback-Leibler (KL) loss with a penalty term. This criterion is different from that for continuous observations in principle, but reduces to the Mallows criterion in the situation. We prove that the corresponding model averaging estimator is asymptotically optimal under certain assumptions. We further extend our concern to the generalized linear mixed-effects model framework and establish associated theory. Numerical experiments illustrate that the proposed method is promising. |
| doi_str_mv | 10.1080/01621459.2015.1115762 |
| format | Article |
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This criterion is different from that for continuous observations in principle, but reduces to the Mallows criterion in the situation. We prove that the corresponding model averaging estimator is asymptotically optimal under certain assumptions. We further extend our concern to the generalized linear mixed-effects model framework and establish associated theory. Numerical experiments illustrate that the proposed method is promising.</description><subject>Asymptotic optimality</subject><subject>Generalized linear models</subject><subject>Kullback-Leibler loss</subject><subject>Misspecification</subject><subject>Penalized generalized weighted least squares (PGWLS)</subject><subject>Prediction accuracy</subject><subject>Statistics</subject><subject>Theory and Methods</subject><issn>0162-1459</issn><issn>1537-274X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kU9LJDEQxcPiwo5_PoLQsBcvPSbppJPcFBldYcSLC95COp1IhkwyJj3q-Ok3Tc96EDGXgrzfqyrqAXCK4BxBDs8hajEiVMwxRHSOEKKsxT_ADNGG1ZiRxwMwG5l6hH6Bw5xXsDzG-Qz4-83g1spXd7E3vrp8MUk9ufBULfL4P7gYKhtTdWNCUbx7N321dMGoNDlypUL_pereTF8vrDV6yHv2GPy0ymdzsq9H4O_14uHqT728v7m9ulzWmuB2qJHoWWdp35pOUdFhwbWx1DAOeYMajSjvmGDQqEYLQbDWPVEtIg0ps2yn2uYInE19Nyk-b00e5NplbbxXwcRtlohzAgUlhBT09yd0FbcplO0KxejIiZGiE6VTzDkZKzepXCftJIJyzED-z0COGch9BsV3OvlWeYjpw0QoxIzicdGLSXehHHmtXmPyvRzUzsdkkwraZdl8P-IfrAeXjQ</recordid><startdate>20161201</startdate><enddate>20161201</enddate><creator>Zhang, Xinyu</creator><creator>Yu, Dalei</creator><creator>Zou, Guohua</creator><creator>Liang, Hua</creator><general>Taylor & Francis</general><general>Taylor & Francis Group,LLC</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope><scope>K9.</scope></search><sort><creationdate>20161201</creationdate><title>Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models</title><author>Zhang, Xinyu ; Yu, Dalei ; Zou, Guohua ; Liang, Hua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c426t-19d7bf5d6eba59b298cef5e7808313c158b7970ea3c9942ccd4a61434fecfba63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Asymptotic optimality</topic><topic>Generalized linear models</topic><topic>Kullback-Leibler loss</topic><topic>Misspecification</topic><topic>Penalized generalized weighted least squares (PGWLS)</topic><topic>Prediction accuracy</topic><topic>Statistics</topic><topic>Theory and Methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Xinyu</creatorcontrib><creatorcontrib>Yu, Dalei</creatorcontrib><creatorcontrib>Zou, Guohua</creatorcontrib><creatorcontrib>Liang, Hua</creatorcontrib><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><jtitle>Journal of the American Statistical Association</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Xinyu</au><au>Yu, Dalei</au><au>Zou, Guohua</au><au>Liang, Hua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models</atitle><jtitle>Journal of the American Statistical Association</jtitle><date>2016-12-01</date><risdate>2016</risdate><volume>111</volume><issue>516</issue><spage>1775</spage><epage>1790</epage><pages>1775-1790</pages><issn>0162-1459</issn><eissn>1537-274X</eissn><coden>JSTNAL</coden><abstract>Considering model averaging estimation in generalized linear models, we propose a weight choice criterion based on the Kullback-Leibler (KL) loss with a penalty term. 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| ispartof | Journal of the American Statistical Association, 2016-12, Vol.111 (516), p.1775-1790 |
| issn | 0162-1459 1537-274X |
| language | eng |
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| source | JSTOR Mathematics & Statistics; JSTOR Archive Collection A-Z Listing; Taylor & Francis:Master (3349 titles) |
| subjects | Asymptotic optimality Generalized linear models Kullback-Leibler loss Misspecification Penalized generalized weighted least squares (PGWLS) Prediction accuracy Statistics Theory and Methods |
| title | Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models |
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