REFERENCES
- Bates, D.M. (2010), “lme4: Mixed-Effects Modeling With R,” [online]. Available at http://lme4.r-forge.r-project.org/book/.
- ——— (2011a), “Computational Methods for Mixed Models,” [online]. . Available at http://cran.r-project.org/web/packages/lme4/vignettes/Theory.pdf.
- ——— (2011b), “Linear Mixed Model Implementation in lme4,” [online]. . Available at http://cran.r-project.org/web/packages/lme4/vignettes/Implementation.pdf.
- Bertsekas, D.P. (1999), Nonlinear Programming, Belmont, MA: Athena Scientific.
- Breheny, P., and Huang, J. (2011), “Coordinate Descent Algorithms for Nonconvex Penalized Regression, With Applications to Biological Feature Selection,” Annals of Applied Statistics, 5, 232–253.
- Breslow, N., and Clayton, D.G. (1993), “Approximate Inference in Generealized Linear Mixed Models,” Journal of the American Statistical Association, 88, 9–25.
- Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004), “Least Angle Regression,” The Annals of Statistics, 32, 407–499.
- Friedman, J., Hastie, T., and Tibshirani, R. (2010), “Regularization Paths for Generalized Linear Models via Coordinate Descent,” Journal of Statistical Software, 33, 1–22.
- Groll, A., and Tutz, G. (in press), “Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation,” Statistics and Computing.
- Ibrahim, J.G., Zhu, H., Garcia, R.I., and Guo, R. (2010), “Fixed and Random Effects Selection in Mixed Effects Models,” Biometrics, 67, 495–503.
- Jiang, J. (2007), Linear and Generalized Linear Mixed Models and Their Applications, New York: Springer.
- Joe, H. (2008), “Accuracy of Laplace Approximation for Discrete Response Mixed Models,” Computational Statistics & Data Analysis, 52, 5066–5074.
- Khalili, A., and Chen, J. (2007), “Variable Selection in Finite Mixture of Regression Models,” Journal of the American Statistical Association, 102, 1025–1038.
- Lai, R., Huang, H.-C., and Lee, T. (2012), “Fixed and Random Effects Selection in Nonparametric Additive Mixed Models,” Electronic Journal of Statistics, 6, 810–842.
- McCullagh, P., and Nelder, J.A. (1989), Generalized Linear Models, London: Chapman & Hall.
- McCulloch, C.E., and Searle, S.R. (2001), Generalized, Linear, and Mixed Models, New York: Wiley.
- Meier, L., van de Geer, S., and Bühlmann, P. (2008), “The Group Lasso for Logistic Regression,” Journal of the Royal Statistical Society, Series B, 70, 53–71.
- Meinshausen, N., and Bühlmann, P. (2006), “High-Dimensional Graphs and Variable Selection With the Lasso,” The Annals of Statistics, 34, 1436–1462.
- Molenberghs, G., and Verbeke, G. (2005), Models for Discrete Longitudinal Data, New York: Springer.
- Ni, X., Zhang, D., and Zhang, H.H. (2010), “Variable Selection for Semiparametric Mixed Models in Longitudinal Studies,” Biometrics, 66, 79–88.
- Pan, W., and Shen, X. (2007), “Penalized Model-Based Clustering With Application to Variable Selection,” Journal of Machine Learning Research, 8, 1145–1164.
- Park, M., and Hastie, T. (2007), “L1-Regularization Path Algorithm for Generalized Linear Models,” Journal of the Royal Statistical Society, Series B, 69, 659–677.
- Schelldorfer, J., Bühlmann, P., and van de Geer, S. (2011), “Estimation for High-Dimensional Linear Mixed-Effects Models Using ℓ1-Penalization,” Scandinavian Journal of Statistics, 38, 197–214.
- Skrondal, A., and Rabe-Hesketh, S. (2004), Generalized Latent Variable Modeling, Boca Raton, FL: Chapman & Hall/CRC.
- Städler, N., Bühlmann, P., and van de Geer, S. (2010), “l1-Penalization for Mixture Regression Models” (with discussion), Test, 19, 209–285.
- Thall, P., and Vail, S. (1990), “Some Covariance Models for Longitudinal Count Data With Overdispersion,” Biometrics, 46, 657–671.
- Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288.
- Tseng, P., and Yun, S. (2009), “A Coordinate Gradient Descent Method for Nonsmooth Separable Minimization,” Mathematical Programming, Series B, 117, 387–423.
- van de Geer, S. (2008), “High-Dimensional Generalized Linear Models and the Lasso,” The Annals of Statistics, 36, 614–645.
- van de Geer, S., Bühlmann, P., and Zhou, S. (2011), “The Adaptive and the Thresholded Lasso for Potentially Misspecified Models (and a Lower Bound for the Lasso),” Electronic Journal of Statistics, 5, 688–749.
- Venables, W.N., and Ripley, B.D. (2002), Modern Applied Statistics With S, New York: Springer.
- Witten, D.M., and Tibshirani, R. (2010), “A Framework for Feature Selection in Clustering,” Journal of the American Statistical Association, 105, 713–726.
- ——— (2011), “Penalized Classification Using Fisher’s Linear Discriminant,” Journal of the Royal Statistical Society, Series B, 73, 753–772.
- Wu, T., and Lange, K. (2008), “Coordinate Descent Algorithms for Lasso Penalized Regression,” Annals of Applied Statistics, 2, 224–244.
- Xue, L., Qu, A., and Zhou, J. (2010), “Consistent Model Selection for Marginal Generalized Additive Model for Correlated Data,” Journal of the American Statistical Association, 105, 1517–1530.
- Yang, H. (2007), “Variable Selection Procedures for Generalized Linear Mixed Models in Longitudinal Data Analysis,” . unpublished Ph.D. dissertation, North Carolina State University.
- Zhou, S. (2010), “Thresholded Lasso for High Dimensional Variable Selection and Statistical Estimation,” arXiv Preprint arXiv:1002.1583v2.
- Zou, H. (2006), “The Adaptive Lasso and Its Oracle Properties,” Journal of the American Statistical Association, 101, 1418–1429.
- Zou, H., Hastie, T., and Tibshirani, R. (2007), “On the ‘Degrees of Freedom’ of the Lasso,” The Annals of Statistics, 35, 2173–2192.