Hypothesis Testing for High-dimensional Regression Models †

[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.