An Investigation of Methods for Handling Missing Data with Penalized Regression
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[1] R. Tibshirani,et al. Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.
[2] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[3] Jérôme Pagès,et al. Multiple imputation in principal component analysis , 2011, Adv. Data Anal. Classif..
[4] Po-Ling Loh,et al. High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity , 2011, NIPS.
[5] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[6] Emmanuel J. Candès,et al. The Power of Convex Relaxation: Near-Optimal Matrix Completion , 2009, IEEE Transactions on Information Theory.
[7] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[8] Cun-Hui Zhang,et al. The sparsity and bias of the Lasso selection in high-dimensional linear regression , 2008, 0808.0967.
[9] R. Tibshirani,et al. PATHWISE COORDINATE OPTIMIZATION , 2007, 0708.1485.