REFERENCES
- Bickel, P., Ritov, Y., and Tsybakov, A. (2009), “Simultaneous Analysis of Lasso and Danzig Selector,” Annals of Statistics, 37, 1705–1732.
- Candes, E., and Tao, T. (2007), “The Dantzig Selector: Statistical Estimation When p is Much Larger Than n,” Annals of Statistics, 35, 2313–2351.
- Consortium, T. W. T. C. C. (2007), “Genome-Wide Association Study of 14, 000 Cases of Seven Common Diseases and 3000 Shared Controls,” Nature, 447, 661–678.
- Donoho, D.L., and Johnstone, I.M. (1998), “Minimax Estimation via Wavelet Shrinkage,” Annals of Statistics, 26, 879–921.
- Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004), “Least Angle Regression,” Annals of Statistics, 32, 407–499.
- 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.
- Friedman, J., Hastie, T., Hofling, H., and Tibshirani, T. (2007), “Pathwise Coordinate Optimization,” Annals of Applied Statistics, 2, 302–332.
- Hastie, T., Tibshirani, R., and Friedman, J. (2009), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, New York: Springer.
- Hebiri, M., and Van de Geer, S. (2011), “The Smooth-Lasso and Other ℓ1 + ℓ2-penalized Methods,” Electronic Journal of Statistics, 5, 1184–1226.
- Huang, J., Ma, S., Li, H., and Zhang, C.H. (2011), “The Sparse Laplacian Shrinkage Estimator for High-Dimensional Regression,” Annals of Statistics, 39, 2021–2046.
- Knight, K., and Fu, W. (2000), “Asymptotics for Lasso-Type Estimators,” Annals of Statistics, 28, 1356–1378.
- Meinshausen, N., and Buhlmann, P. (2010), “Stability Selection,” Journal of the Royal Statistical Society, Series B, 72, 417–473.
- Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288.
- Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., and Knight, K. (2005), “Sparsity and Smoothness via the Fused Lasso,” Journal of the Royal Statistical Society, Series B, 67, 91–108.
- Tibshirani, R., and Taylor, J.2011
- Tibshirani, R., and Wang, P. (2007), “Spatial Smoothing and Hot Spot Detection for CGH Data using the Fused Lasso,” Biostatistics, 9, 18–29.
- Tsybakov, A.B. (2009), Introduction to Nonparametric Estimation, New York: Springer.
- van de Geer, S., and Buhlmann, P. (2009), “On the Conditions used to Prove Oracle Results for the Lasso,” Electronic Journal of Statistics, 3, 1360–1392.
- Yang, C., Zhou, X.W., Wan, X., Yang, Q., Xue, H., and Yu, W.C. (2011), “Identifying Disease-Associated SNP clusters via Contiguous Outlier Detection,” Bioinformatics, 27, 2578–2585.
- 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.
- Zhang, C.H. (2010), “Nearly Unbiased Variable Selection Under Minimax Concave Penalty,” Annals of Statistics, 38, 894–942.
- Zou, H. (2006), “The Adaptive Lasso and its Oracle Properties,” Journal of the American Statistical Association, 97, 210–221.
- Zou, H., and Hastie, T. (2005), “Regularization and Variable Selection via the Elastic Net,” Journal of the Royal Statistical Society, Series B, 67, 301–320.