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[1] R. Tibshirani,et al. A Study of Error Variance Estimation in Lasso Regression , 2013, 1311.5274.
[2] Ji Zhu,et al. Regularized Multivariate Regression for Identifying Master Predictors with Application to Integrative Genomics Study of Breast Cancer. , 2008, The annals of applied statistics.
[3] Sara van de Geer,et al. Statistical Theory for High-Dimensional Models , 2014, 1409.8557.
[4] Peter Bühlmann,et al. p-Values for High-Dimensional Regression , 2008, 0811.2177.
[5] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[6] Lu Tian,et al. A Perturbation Method for Inference on Regularized Regression Estimates , 2011, Journal of the American Statistical Association.
[7] Peng Zhao,et al. On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..
[8] Qi Zhang,et al. Optimality of graphlet screening in high dimensional variable selection , 2012, J. Mach. Learn. Res..
[9] Xiaoming Huo,et al. Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.
[10] Yichao Wu,et al. Ultrahigh Dimensional Feature Selection: Beyond The Linear Model , 2009, J. Mach. Learn. Res..
[11] MontanariAndrea,et al. Confidence intervals and hypothesis testing for high-dimensional regression , 2014 .
[12] R. Tibshirani. A signicance test for the lasso , 2014 .
[13] Peter Bühlmann,et al. High-Dimensional Statistics with a View Toward Applications in Biology , 2014 .
[14] Jianqing Fan,et al. Variance estimation using refitted cross‐validation in ultrahigh dimensional regression , 2010, Journal of the Royal Statistical Society. Series B, Statistical methodology.
[15] E. Candès,et al. Near-ideal model selection by ℓ1 minimization , 2008, 0801.0345.
[16] L. Wasserman,et al. HIGH DIMENSIONAL VARIABLE SELECTION. , 2007, Annals of statistics.
[17] Jiashun Jin,et al. Partial Correlation Screening for Estimating Large Precision Matrices, with Applications to Classification , 2014, 1409.3301.
[18] Adel Javanmard,et al. Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models , 2013 .
[19] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[20] Kengo Kato,et al. Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors , 2012, 1212.6906.
[21] Martin J. Wainwright,et al. Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$ -Constrained Quadratic Programming (Lasso) , 2009, IEEE Transactions on Information Theory.
[22] 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).
[23] Christopher R. Genovese,et al. Asymptotic theory for density ridges , 2014, 1406.5663.
[24] J WainwrightMartin. Sharp thresholds for high-dimensional and noisy sparsity recovery using l1-constrained quadratic programming (Lasso) , 2009 .
[25] Cun-Hui Zhang. Nearly unbiased variable selection under minimax concave penalty , 2010, 1002.4734.
[26] P. Bühlmann. Statistical significance in high-dimensional linear models , 2013 .
[27] S. Geer,et al. On the conditions used to prove oracle results for the Lasso , 2009, 0910.0722.
[28] R. Tibshirani,et al. A SIGNIFICANCE TEST FOR THE LASSO. , 2013, Annals of statistics.
[29] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[30] Scott Chen,et al. Examples of basis pursuit , 1995, Optics + Photonics.
[31] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[32] P. Bickel,et al. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR , 2008, 0801.1095.
[33] S. Geer,et al. Confidence intervals for high-dimensional inverse covariance estimation , 2014, 1403.6752.
[34] Patrick Seemann,et al. Matrix Factorization Techniques for Recommender Systems , 2014 .
[35] E. Lehmann. Testing Statistical Hypotheses , 1960 .
[36] Shuheng Zhou,et al. 25th Annual Conference on Learning Theory Reconstruction from Anisotropic Random Measurements , 2022 .
[37] M. Lustig,et al. Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.
[38] S. Geer,et al. ℓ1-penalization for mixture regression models , 2010, 1202.6046.
[39] Sara van de Geer,et al. Statistics for High-Dimensional Data: Methods, Theory and Applications , 2011 .
[40] Yehuda Koren,et al. Matrix Factorization Techniques for Recommender Systems , 2009, Computer.
[41] Adel Javanmard,et al. Hypothesis Testing in High-Dimensional Regression Under the Gaussian Random Design Model: Asymptotic Theory , 2013, IEEE Transactions on Information Theory.
[42] N. S. Barnett,et al. Private communication , 1969 .
[43] Peter Buhlmann. Statistical significance in high-dimensional linear models , 2012, 1202.1377.
[44] S. Geer,et al. On asymptotically optimal confidence regions and tests for high-dimensional models , 2013, 1303.0518.
[45] Roman Vershynin,et al. Introduction to the non-asymptotic analysis of random matrices , 2010, Compressed Sensing.
[46] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[47] Jianqing Fan,et al. Sure independence screening for ultrahigh dimensional feature space , 2006, math/0612857.
[48] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[49] Martin J. Wainwright,et al. A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers , 2009, NIPS.
[50] A. Belloni,et al. Least Squares After Model Selection in High-Dimensional Sparse Models , 2009, 1001.0188.
[51] Mehmet Caner,et al. Asymptotically Honest Confidence Regions for High Dimensional Parameters by the Desparsified Conservative Lasso , 2014, 1410.4208.
[52] R. Tibshirani,et al. Adaptive testing for the graphical lasso , 2013, 1307.4765.
[53] Lee H. Dicker,et al. Residual variance and the signal-to-noise ratio in high-dimensional linear models , 2012, 1209.0012.
[54] Cun-Hui Zhang,et al. Confidence intervals for low dimensional parameters in high dimensional linear models , 2011, 1110.2563.
[55] R. Tibshirani,et al. Exact Post-selection Inference for Forward Stepwise and Least Angle Regression , 2014 .
[56] E. L. Lehmann,et al. Theory of point estimation , 1950 .
[57] Larry Wasserman,et al. All of Statistics: A Concise Course in Statistical Inference , 2004 .
[58] Harrison H. Zhou,et al. Asymptotic normality and optimalities in estimation of large Gaussian graphical models , 2013, 1309.6024.
[59] Sara van de Geer,et al. Statistics for High-Dimensional Data , 2011 .
[60] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[61] Y. Ritov,et al. Persistence in high-dimensional linear predictor selection and the virtue of overparametrization , 2004 .
[62] Isaac Dialsingh,et al. Large-scale inference: empirical Bayes methods for estimation, testing, and prediction , 2012 .
[63] Larry A. Wasserman,et al. Estimating Undirected Graphs Under Weak Assumptions , 2013, ArXiv.
[64] P. Hall,et al. Permutation tests for equality of distributions in high‐dimensional settings , 2002 .
[65] Andrea Montanari,et al. Estimating LASSO Risk and Noise Level , 2013, NIPS.
[66] Cun-Hui Zhang,et al. Scaled sparse linear regression , 2011, 1104.4595.
[67] N. Meinshausen,et al. Stability selection , 2008, 0809.2932.
[68] Victor Chernozhukov,et al. Inference on Treatment Effects after Selection Amongst High-Dimensional Controls , 2011 .
[69] Guang Cheng,et al. Bootstrapping High Dimensional Time Series , 2014, 1406.1037.
[70] Stéphane Mallat,et al. Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..
[71] Bradley Efron,et al. Large-scale inference , 2010 .