Fixed and Random Effects Selection in Mixed Effects Models

Biometrics

Volumn 67, Issue 2, 2011, Pages 495-503

  Ibrahim, Joseph G ^a   Zhu, Hongtu ^a   Garcia, Ramon I ^a   Guo, Ruixin ^a  

^a IL   (United States)

Author keywords

ALASSO; Cholesky decomposition; EM algorithm; Mixed effects selection; Penalized likelihood; SCAD

Indexed keywords

BIOMETRICS; MAXIMUM LIKELIHOOD ESTIMATION;

ADAPTIVE LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR; CHOLESKY DECOMPOSITION; EM ALGORITHMS; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATORS; MIXED EFFECT SELECTION; MIXED EFFECTS; PENALIZED LIKELIHOOD; PENALIZED-LIKELIHOOD; RANDOM EFFECTS; SMOOTHLY CLIPPED ABSOLUTE DEVIATION;

RANDOM PROCESSES;

ALGORITHM; DATA SET; ESTIMATION METHOD; LINEARITY; NUMERICAL MODEL; STATISTICAL ANALYSIS;

EID: 79959368569     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2010.01463.x     Document Type: Article

References (37)

1. Bondell, H. D., Krishna, A., and Ghosh, S. K. (2010). Joint variable selection for fixed and random effects in linear mixed-effects models. To apper inBiometrics.
2. Breheny, P. and Huang, J. (2009). Penalized methods for bi-level variable selection. Statistics and its Interface 2, 369-380.
3. Cai, J., Fan, J., Li, R., and Zhou, H. (2005). Variable selection for multivariate failure time data. Biometrika 92, 303-316.
4. Chen, Z. and Dunson, D. (2003). Random effects selection in linear mixed models. Biometrics 59, 762-769.
5. Claeskens, G. and Consentino, F. (2008). Variable selection with incomplete covariate data. Biometrics 64, 1062-1096.
6. Daniels, M. J. and Kass, R. E. (1999). Nonconjugate Bayesian estimation of covariance matrices and its use in hierarchical models. Journal of the American Statistical Association 94, 1254-1263.
7. Daniels, M. J. and Kass, R. E. (2001). Shrinkage estimators for covariance matrices. Biometrics 57, 1173-1184.
8. Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 96, 1348-1360.
9. Fan, J. and Li, R. (2002). Variable selection for Cox's proportional hazards model and frailty model. Annals of Statistics 30, 74-99.
10. Fan, J. and Li, R. (2004). New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis. Journal of the American Statistical Association 99, 710-723.
11. Fu, W. (1998). Penalized regressions: The bridge versus the lasso. Journal of Computational and Graphical Statistics 7, 375-384.
12. Garcia, R. I., Ibrahim, J. G., and Zhu, H. (2010a). Variable selection for regression models with missing data. Statistica Sinica 20, 149-165.
13. Garcia, R. I., Ibrahim, J. G., and Zhu, H. (2010b). Variable selection in the Cox regression model with covariates missing at random. Biometrics 66, 97-104.
14. Gurka, M. J. (2006). Selecting the best linear mixed model under REML. American Statistician 60, 19-26.
15. Hunter, D. R. and Li, R. (2005). Variable selection using MM algorithms. Annals of Statistics 33, 1617-1642.
16. Ibrahim, J. G. (1990). Incomplete data in generalized linear models. Journal of the American Statistical Association 85, 765-769.
17. Ibrahim, J. G. and Lipsitz, S. R. (1996). Parameter estimation from incomplete data in binomial regression when the missing data mechanism is nonignorable. Biometrics 52, 1071-1078.
18. Ibrahim, J. G., Chen, M. H., and Lipsitz, S. R. (1999). Monte Carlo EM for missing covariates in parametric regression models. Biometrics 55, 591-596.
19. Ibrahim, J. G., Zhu, H., and Tang, N. (2008). Model selection criteria for missing-data problems using the EM algorithm. Journal of the American Statistical Association 103, 1648-1658.
20. Johnson, B., Lin, D. Y., and Zeng, D. (2008). Penalized estimating functions and variable selection in semiparametric regression models. Journal of the American Statistical Association 103, 672-680.
21. Keselman, H. J., Algina, J., Kowalchuk, R. K., and Wolfinger, R. D. (1998). A comparison of two approaches for selecting covariance structures in the analysis of repeated measurements. Communications in Statistics-Simulation and Computation 27, 591-604.
22. Kowalchuck, R. K., Keselman, H. J., Algina, J., and Wolfinger, R. D. (2004). The analysis of repeated measurements with mixed-model adjusted F tests. Educational and Psychological Measurement 64, 224-242.
23. Liang, H., Wu, H., and Zou, G. (2008). A note on conditional AIC for linear mixed effects-models. Biometrika 95, 773-778.
24. Little, R. J. A. and Schluchter, M. (1985). Maximum likelihood estimation for mixed continuous and categorical data with missing values. Biometrika 72, 497-512.
25. Meng, X. L. and Rubin, D. B. (1993). Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika 80, 267-278.
26. Ni, X., Zhang, D., and Zhang, H. (2009). Variable selection for semiparametric mixed models in longitudinal studies. Biometrics 66, 79-88.
27. Qu, A. and Li, R. (2006). Quadratic inference functions for varying-coefficient models with longitudinal data. Biometrics 62, 379-391.
28. Stier, D. M., Leventhal, J. M., Berg, A. T., Johnson, L., and Mezger, J. (1993). Are children born to young mothers at increased risk of maltreatment? Pediatrics 91, 642-648.
29. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 58, 267-288.
30. Wang, H., Li, R., and Tsai, C. L. (2007). Tuning parameter selector for the smoothly clipped absolute deviation method. Biometrika 94, 553-568.
31. Wasserman, D. R. and Leventhal, J. M. (1993). Maltreatment of children born to cocaine-dependent mothers. American Journal of Diseases of Children 147, 1324-1328.
32. Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B 68, 49-67.
33. Zhang, H. and Lu, W. (2007). Adaptive-LASSO for Cox's proportional hazards model. Biometrika 94, 1-13.
34. Zhao, P., Rocha, G., and Yu, B. (2009). The composite absolute penalties family for grouped and hierarchical variable selection. Annals of Statistics 37, 3468-3497.
35. Zhu, H. T. and Zhang, H. P. (2006). Generalized score test for homogeneity for mixed effects models. Annals of Statistics 34, 1545-1569.
36. Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association 101, 1418-1429.
37. Zou, H. and Li, R. (2008). One-step sparse estimates in noncancave penalized likelihood models. Annals of Statistics 36, 1509-1533.