Fixed and Random Effects Selection in Mixed Effects Models
We consider selecting both fixed and random effects in a general class of mixed effects models using maximum penalized likelihood (MPL) estimation along with the smoothly clipped absolute deviation (SCAD) and adaptive least absolute shrinkage and selection operator (ALASSO) penalty functions. The MP...
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| Veröffentlicht in: | Biometrics 2011-06, Vol.67 (2), p.495-503 |
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| creator | Ibrahim, Joseph G. Zhu, Hongtu Garcia, Ramon I. Guo, Ruixin |
| description | We consider selecting both fixed and random effects in a general class of mixed effects models using maximum penalized likelihood (MPL) estimation along with the smoothly clipped absolute deviation (SCAD) and adaptive least absolute shrinkage and selection operator (ALASSO) penalty functions. The MPL estimates are shown to possess consistency and sparsity properties and asymptotic normality. A model selection criterion, called the IC Q statistic, is proposed for selecting the penalty parameters (Ibrahim, Zhu, and Tang, 2008, Journal of the American Statistical Association 103, 1648-1658). The variable selection procedure based on IC Q is shown to consistently select important fixed and random effects. The methodology is very general and can be applied to numerous situations involving random effects, including generalized linear mixed models. Simulation studies and a real data set from a Yale infant growth study are used to illustrate the proposed methodology. |
| doi_str_mv | 10.1111/j.1541-0420.2010.01463.x |
| format | Article |
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The MPL estimates are shown to possess consistency and sparsity properties and asymptotic normality. A model selection criterion, called the IC Q statistic, is proposed for selecting the penalty parameters (Ibrahim, Zhu, and Tang, 2008, Journal of the American Statistical Association 103, 1648-1658). The variable selection procedure based on IC Q is shown to consistently select important fixed and random effects. The methodology is very general and can be applied to numerous situations involving random effects, including generalized linear mixed models. Simulation studies and a real data set from a Yale infant growth study are used to illustrate the proposed methodology.</description><subject>ALASSO</subject><subject>Bioinformatics</subject><subject>BIOMETRIC METHODOLOGY</subject><subject>Biometrics</subject><subject>Biometry - methods</subject><subject>Cholesky decomposition</subject><subject>Computer Simulation</subject><subject>Covariance matrices</subject><subject>Datasets</subject><subject>EM algorithm</subject><subject>Estimation methods</subject><subject>Estimators</subject><subject>Gestational age</subject><subject>Growth</subject><subject>Humans</subject><subject>ICQ criterion</subject><subject>Infant</subject><subject>Infant growth</subject><subject>Infants</subject><subject>Likelihood Functions</subject><subject>Mathematical models</subject><subject>Maximum likelihood estimation</subject><subject>Medical research</subject><subject>Mixed effects selection</subject><subject>Models, Statistical</subject><subject>Penalized likelihood</subject><subject>Penalty function</subject><subject>SCAD</subject><subject>Statistical methods</subject><issn>0006-341X</issn><issn>1541-0420</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNUU1v1DAQtRCo3X78BKqIC6cs_oqTcECCbbtU6lLR8tHbyEmc4jQbFzsL23_PpNuGlhM-eGy_N29m_AiJGJ0yXG-aKUski6nkdMopvlImlZiun5HJCDwnE0qpioVkl9tkJ4QGr3lC-RbZ5lQpngk2IW-P7dpUke6q6Bw3t4yO6tqUfYguTIvRui6yXbS4Yz1AC1eZNuyRF7Vug9m_j7vk6_HRl9nH-PRsfjJ7fxqXiWIiTrJEUlqp1EhVYHO5zJnSPGPSZEqYVOdJntdZpXVJy4QVFAdKC1kYKQqd56XYJe82ujerYmmq0nS91y3ceLvU_hactvAU6ewPuHK_QFCsJjgKvL4X8O7nyoQeljaUpm11Z9wqQJYKwSTWRearf5iNW_kOp0MSfhhlPEVStiGV3oXgTT22wigM9kADgwswuACDPXBnD6wx9eDxKGPigx9_Z_1tW3P738Lw4eRsMRxR4OVGoAm986OAZFxymQ3Nxxvcht6sR1z7a1CpSBP4_mkO3z6fX8zZ7BCk-AO-sLTY</recordid><startdate>201106</startdate><enddate>201106</enddate><creator>Ibrahim, Joseph G.</creator><creator>Zhu, Hongtu</creator><creator>Garcia, Ramon I.</creator><creator>Guo, Ruixin</creator><general>Blackwell Publishing Inc</general><general>Wiley-Blackwell</general><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>201106</creationdate><title>Fixed and Random Effects Selection in Mixed Effects Models</title><author>Ibrahim, Joseph G. ; 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| source | MEDLINE; JSTOR Mathematics & Statistics; JSTOR Archive Collection A-Z Listing; Oxford University Press Journals All Titles (1996-Current); Wiley Online Library All Journals |
| subjects | ALASSO Bioinformatics BIOMETRIC METHODOLOGY Biometrics Biometry - methods Cholesky decomposition Computer Simulation Covariance matrices Datasets EM algorithm Estimation methods Estimators Gestational age Growth Humans ICQ criterion Infant Infant growth Infants Likelihood Functions Mathematical models Maximum likelihood estimation Medical research Mixed effects selection Models, Statistical Penalized likelihood Penalty function SCAD Statistical methods |
| title | Fixed and Random Effects Selection in Mixed Effects Models |
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