Hypothesis Testing for High-dimensional Regression Models †
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[1] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[2] S. Geer,et al. On asymptotically optimal confidence regions and tests for high-dimensional models , 2013, 1303.0518.
[3] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[4] Q. Shao,et al. Phase Transition and Regularized Bootstrap in Large Scale $t$-tests with False Discovery Rate Control , 2013, 1310.4371.
[5] Cun-Hui Zhang. Nearly unbiased variable selection under minimax concave penalty , 2010, 1002.4734.
[6] Joel A. Tropp,et al. Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.
[7] Yongdai Kim,et al. Smoothly Clipped Absolute Deviation on High Dimensions , 2008 .
[8] Zehua Chen,et al. Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces , 2011 .
[9] Xiaoming Yuan,et al. An R Package flare for High Dimensional Linear Regression and Precision Matrix Estimation , 2012 .
[10] A. Belloni,et al. Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming , 2011 .
[11] Zehua Chen,et al. Sequential Lasso Cum EBIC for Feature Selection With Ultra-High Dimensional Feature Space , 2014 .
[12] Weidong Liu. Gaussian graphical model estimation with false discovery rate control , 2013, 1306.0976.
[13] Anja Vogler,et al. An Introduction to Multivariate Statistical Analysis , 2004 .
[14] T. W. Anderson,et al. An Introduction to Multivariate Statistical Analysis , 1959 .
[15] Cun-Hui Zhang,et al. Adaptive Lasso for sparse high-dimensional regression models , 2008 .
[16] Gareth M. James,et al. Improved variable selection with Forward-Lasso adaptive shrinkage , 2011, 1104.3390.
[17] Peng Zhao,et al. On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..
[18] Adel Javanmard,et al. Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models , 2013 .
[19] Lie Wang,et al. Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise , 2011, IEEE Transactions on Information Theory.
[20] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[21] Joel A. Tropp,et al. Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.
[22] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[23] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[24] Adel Javanmard,et al. Nearly optimal sample size in hypothesis testing for high-dimensional regression , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[25] G. Wahba,et al. A NOTE ON THE LASSO AND RELATED PROCEDURES IN MODEL SELECTION , 2006 .
[26] Pei Wang,et al. Partial Correlation Estimation by Joint Sparse Regression Models , 2008, Journal of the American Statistical Association.
[27] Tong Zhang,et al. Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations , 2011, IEEE Transactions on Information Theory.
[28] Deanna Needell,et al. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.