MOMENT-BASED METHOD FOR RANDOM EFFECTS SELECTION IN LINEAR MIXED MODELS.
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[1] H. Bondell,et al. Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR , 2008, Biometrics.
[2] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[3] Hao Helen Zhang,et al. Adaptive Lasso for Cox's proportional hazards model , 2007 .
[4] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[5] H. Bondell,et al. Joint Variable Selection for Fixed and Random Effects in Linear Mixed‐Effects Models , 2010, Biometrics.
[6] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[7] R. Wolfinger. Covariance structure selection in general mixed models , 1993 .
[8] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[9] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[10] L. Qi,et al. Global Convergence of Gauss-Newton-MBFGS Method for Solving the Nonlinear Least Squares Problem , 2010 .
[11] R. Jennrich,et al. Unbalanced repeated-measures models with structured covariance matrices. , 1986, Biometrics.
[12] J. Heron. Applied Longitudinal Data Analysis for Epidemiology—A Practical Guide. Jos W R Twisk, Cambridge: Cambridge University Press, 2003, pp. 318 £24.95 (PB) ISBN: 0-521-52580-2, £65.00 (HB) ISBN: 0-521-81976-8. , 2004 .
[13] F. Vaida,et al. Conditional Akaike information for mixed-effects models , 2005 .
[14] D. Dunson,et al. Random Effects Selection in Linear Mixed Models , 2003, Biometrics.
[15] D. Bates,et al. Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data , 1988 .
[16] J. Ware,et al. Random-effects models for longitudinal data. , 1982, Biometrics.
[17] Adam J. Rothman,et al. Generalized Thresholding of Large Covariance Matrices , 2009 .
[18] L. Breiman. Better subset regression using the nonnegative garrote , 1995 .
[19] Han C. G. Kemper,et al. The Amsterdam growth study : a longitudinal analysis of health, fitness, and lifestyle , 1995 .
[20] H. Akaike. Maximum likelihood identification of Gaussian autoregressive moving average models , 1973 .
[21] Johan Löfberg,et al. YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .
[22] Kenneth Holmstrom,et al. The TOMLAB Optimization Environment in Matlab , 1999 .
[23] Calyampudi R. Rao,et al. A strongly consistent procedure for model selection in a regression problem , 1989 .
[24] X. Niu,et al. Selecting mixed-effects models based on a generalized information criterion , 2006 .
[25] Satkartar K. Kinney,et al. Fixed and Random Effects Selection in Linear and Logistic Models , 2007, Biometrics.
[26] P. Diggle. Analysis of Longitudinal Data , 1995 .
[27] Kenneth Holmström,et al. The TOMLAB Optimization Environment , 2004 .
[28] Jos F. Sturm,et al. A Matlab toolbox for optimization over symmetric cones , 1999 .
[29] Lexin Li,et al. Longitudinal data model selection , 2006, Comput. Stat. Data Anal..
[30] Hansheng Wang,et al. Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso , 2007 .
[31] J. Twisk,et al. Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide , 2003 .
[32] H. Bondell,et al. Simultaneous regression shrinkage , variable selection and clustering of predictors with OSCAR , 2006 .