Moment-based method for random effects selection in linear mixed models

Statistica Sinica

Volumn 22, Issue 4, 2012, Pages 1539-1562

  Ahn, Mihye ^a   Zhang, Hao Helen ^a   Lu, Wenbin ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

Hard thresholding; Linear mixed model; Shrinkage estimation; Variance component selection

Indexed keywords

EID: 84866866508     PISSN: 10170405     EISSN: None     Source Type: Journal    
DOI: 10.5705/ss.2011.054     Document Type: Article

References (29)

1. Akaike, H. (1973). Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika 60, 255-265.
2. Azari, R., Li, L. and Tsai, C.-L. (2006). Longitudinal data model selection. Comput. Statist. Data Anal. 50, 3053-3066.
3. Bondell, H., Krishna, A. and Ghosh, S. (2010). Joint variable selection of fixed and random effects in linear mixed-effects models. Biometrics 66, 1069-1077.
4. Bondell, H. D. and Reich, B. J. (2008). Simultaneous regression shrinkage, variable selection and clustering of predictors with OSCAR. Biometrics 64, 115-123.
5. Breiman, L. (1995). Better subset regression using the nonnegative garrote. Technometrics 37, 373-384.
6. Chen, Z. and Dunson, D. (2003). Random effects selection in linear mixed models. Biometrics 59, 762-769.
7. Diggle, P., Liang, K. and Zeger, S. (1994). Analysis of Longitudinal Data. Oxford University Press, New York.
8. Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. J. Amer. Statist. Assoc. 96, 1348-1360.
9. Holmström, K. (1999). The TOMLAB optimization environment in MATLAB. Adv. Model. Optim. 1, 47-69.
10. Jennrich, R. I. and Schluchter, M. D. (1986). Unbalanced repeated-measures models with structured covariance matrices. Biometrics 42, 805-820.
11. Kemper, H. (1995). The Amsterdam growth study: A longitudinal analysis of health, fitness and lifestyle. In HK Sport Science Monograph Series, 6, Human Kinetics Publishers, Champaign IL.
12. Kinney, S. K. and Dunson, D. B. (2007). Fixed and random effects selection in linear and logistic models. Biometrics 63, 690-698.
13. Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics 38, 963-974.
14. Lindstrom, M. J. and Bates, D. M. (1988). Newton-Raphson and EM algorithms for linear mixed effects models for repeated measures data. J. Amer. Statist. Assoc. 83, 1014-1022.
15. Löfberg, J. (2004). YALMIP: A toolbox for modeling and optimization in MATLAB. In Proc. of the CACSD Conf., Taipei, Taiwan.
16. Niu, F. and Pu, P. (2006). Selecting mixed-effects models based on generalized information criterion. J. Multivariate Anal. 97, 733-758.
17. Rao, C. R. and Wu, Y. (1989). A strongly consistent procedure for model selection in regression problems. Biometrika 76, 369-374.
18. Rothman, A., Levina, E. and Zhu, J. (2009). Generalized thresholding of large covariance matrices. J. Amer. Statist. Assoc. 104, 177-186.
19. Schwarz, G. (1978). Estimating the dimension of a model. Ann. Statist. 6, 461-464.
20. Sturm, J. F. (1999). Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones. Optim. Meth. Softw. 11-12, 625-653.
21. Tibshirani, R. J. (1996). Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. Ser. B 58, 267-288.
22. Twisk, J. W. (2003). Applied Longitudinal Data Analysis for Epidemiology-Practical Guide. Cambridge University press, New York.
23. Vaida, F. and Blanchard, S. (2005). Conditional Akaike information for mixed-effects models. Biometrika 92, 351-370.
24. Wang, F., Li, D.-H. and Qi, L. (2010). Global convergence of Gauss-Newton-MBFGS method for solving the nonlinear least squares problem. Adv. Model. Optim. 12(1), 1-20.
25. Wang, F., Li, D.-H. and Jiang, G. (2007). Robust regression shrinkage and consistent variable selection via the lad-lasso. J. Bus. Econ. Stat. 20, 347-355.
26. Wolfinger, R. D. (1993). Covariance structure selection in general mixed models. Comm. Statist. Simul. Comput. 22, 1079-1106.
27. Zhang, H. H. and Lu, W. (2007). Adaptive lasso for Cox's proportional hazards model. Biometrika 94, 691-703.
28. Zou, H. (2006). The adaptive lasso and its oracle properties. J. Amer. Statist. Assoc. 101, 1418-1429.
29. Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. J. Roy. Statist. Soc. Ser. B 67, 301-320.