Pivotal estimation via square-root Lasso in nonparametric regression
暂无分享,去创建一个
[1] Karim Lounici. Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators , 2008, 0801.4610.
[2] A. Belloni,et al. SPARSE MODELS AND METHODS FOR OPTIMAL INSTRUMENTS WITH AN APPLICATION TO EMINENT DOMAIN , 2012 .
[3] J WainwrightMartin. Sharp thresholds for high-dimensional and noisy sparsity recovery using l1-constrained quadratic programming (Lasso) , 2009 .
[4] P. Robinson. ROOT-N-CONSISTENT SEMIPARAMETRIC REGRESSION , 1988 .
[5] S. Geer,et al. Rejoinder: ℓ1-penalization for mixture regression models , 2010 .
[6] Alexandre B. Tsybakov,et al. Introduction to Nonparametric Estimation , 2008, Springer series in statistics.
[7] V. Koltchinskii. Sparsity in penalized empirical risk minimization , 2009 .
[8] Yurii Nesterov,et al. Interior-point polynomial algorithms in convex programming , 1994, Siam studies in applied mathematics.
[9] Stéphane Chrétien,et al. Sparse Recovery With Unknown Variance: A LASSO-Type Approach , 2011, IEEE Transactions on Information Theory.
[10] B. V. Bahr,et al. Inequalities for the $r$th Absolute Moment of a Sum of Random Variables, $1 \leqq r \leqq 2$ , 1965 .
[11] WangLie. The L1 penalized LAD estimator for high dimensional linear regression , 2013 .
[12] Florentina Bunea,et al. Aggregation and sparsity via 1 penalized least squares , 2006 .
[13] Lutz Dümbgen,et al. Nemirovski's Inequalities Revisited , 2008, Am. Math. Mon..
[14] A. Tsybakov,et al. Sparsity oracle inequalities for the Lasso , 2007, 0705.3308.
[15] Victor Chernozhukov,et al. Inference on Treatment Effects after Selection Amongst High-Dimensional Controls , 2011 .
[16] Sylvie Huet,et al. High-dimensional regression with unknown variance , 2011, 1109.5587.
[17] Christian Hansen,et al. Lasso Methods for Gaussian Instrumental Variables Models , 2010, 1012.1297.
[18] A. Belloni,et al. Least Squares After Model Selection in High-Dimensional Sparse Models , 2009, 1001.0188.
[19] A. Tsybakov,et al. Aggregation for Gaussian regression , 2007, 0710.3654.
[20] Harrison H. Zhou,et al. Asymptotic normality and optimalities in estimation of large Gaussian graphical models , 2013, 1309.6024.
[21] Takeshi Amemiya,et al. The Maximum Likelihood and the Nonlinear Three-Stage Least Squares Estimator in the General Nonlinear Simultaneous Equation Model , 1977 .
[22] Gary Chamberlain,et al. Efficiency Bounds for Semiparametric Regression , 1992 .
[23] Yin Chen,et al. Fused sparsity and robust estimation for linear models with unknown variance , 2012, NIPS.
[24] Bing-Yi Jing,et al. Self-normalized Cramér-type large deviations for independent random variables , 2003 .
[25] S. Geer,et al. On the conditions used to prove oracle results for the Lasso , 2009, 0910.0722.
[26] S. Gaiffas,et al. High Dimensional Matrix Estimation With Unknown Variance Of The Noise , 2011, 1112.3055.
[27] Kengo Kato,et al. Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors , 2013 .
[28] Cun-Hui Zhang,et al. Confidence Intervals for Low-Dimensional Parameters With High-Dimensional Data , 2011 .
[29] Jianqing Fan,et al. Sure independence screening for ultrahigh dimensional feature space , 2006, math/0612857.
[30] Jon A. Wellner,et al. Weak Convergence and Empirical Processes: With Applications to Statistics , 1996 .
[31] A. Belloni,et al. Inference on Treatment Effects after Selection Amongst High-Dimensional Controls , 2011, 1201.0224.
[32] James Renegar,et al. A mathematical view of interior-point methods in convex optimization , 2001, MPS-SIAM series on optimization.
[33] B. M. Pötscher,et al. CAN ONE ESTIMATE THE UNCONDITIONAL DISTRIBUTION OF POST-MODEL-SELECTION ESTIMATORS? , 2007, Econometric Theory.
[34] William Feller,et al. An Introduction to Probability Theory and Its Applications , 1951 .
[35] S. Geer. HIGH-DIMENSIONAL GENERALIZED LINEAR MODELS AND THE LASSO , 2008, 0804.0703.
[36] A. Belloni,et al. Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming , 2010, 1009.5689.
[37] Massimiliano Pontil,et al. Taking Advantage of Sparsity in Multi-Task Learning , 2009, COLT.
[38] A. V. D. Vaart,et al. Asymptotic Statistics: U -Statistics , 1998 .
[39] N. Meinshausen,et al. LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA , 2008, 0806.0145.
[40] Zhaosong Lu. Gradient based method for cone programming with application to large-scale compressed sensing , 2008 .
[41] Kengo Kato,et al. Uniform post selection inference for LAD regression models , 2013 .
[42] Sara van de Geer,et al. Statistics for High-Dimensional Data: Methods, Theory and Applications , 2011 .
[43] Emmanuel J. Candès,et al. Templates for convex cone problems with applications to sparse signal recovery , 2010, Math. Program. Comput..
[44] Kengo Kato,et al. Uniform post selection inference for LAD regression and other z-estimation problems , 2013 .
[45] M. Farrell. Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations , 2013, 1309.4686.
[46] 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.
[47] Cun-Hui Zhang,et al. Scaled sparse linear regression , 2011, 1104.4595.
[48] Tze Leung Lai,et al. Self-Normalized Processes , 2009 .
[49] A. Belloni,et al. L1-Penalized Quantile Regression in High Dimensional Sparse Models , 2009, 0904.2931.
[50] P. Bickel,et al. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR , 2008, 0801.1095.
[51] A. Belloni,et al. Program evaluation with high-dimensional data , 2013 .
[52] A. Tsybakov,et al. Sparse recovery under matrix uncertainty , 2008, 0812.2818.
[53] Yurii Nesterov,et al. Smooth minimization of non-smooth functions , 2005, Math. Program..
[54] A. Tsybakov,et al. High-dimensional instrumental variables regression and confidence sets -- v2/2012 , 2018, 1812.11330.
[55] S. Geer,et al. ℓ1-penalization for mixture regression models , 2010, 1202.6046.
[56] H. Rosenthal. On the subspaces ofLp(p>2) spanned by sequences of independent random variables , 1970 .
[57] A. Belloni,et al. Inference for High-Dimensional Sparse Econometric Models , 2011, 1201.0220.
[58] E. Candès,et al. Near-ideal model selection by ℓ1 minimization , 2008, 0801.0345.
[59] Cun-Hui Zhang,et al. The sparsity and bias of the Lasso selection in high-dimensional linear regression , 2008, 0808.0967.
[60] Yurii Nesterov,et al. Dual extrapolation and its applications to solving variational inequalities and related problems , 2003, Math. Program..
[61] M. Kosorok. Introduction to Empirical Processes and Semiparametric Inference , 2008 .
[62] S. Geer,et al. On asymptotically optimal confidence regions and tests for high-dimensional models , 2013, 1303.0518.
[63] A. Belloni,et al. Pivotal estimation via square-root Lasso in nonparametric regression , 2011, 1105.1475.
[64] Victor Chernozhukov,et al. High Dimensional Sparse Econometric Models: An Introduction , 2011, 1106.5242.
[65] Kengo Kato,et al. Gaussian approximation of suprema of empirical processes , 2012, 1212.6885.
[66] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[67] P. J. Huber. The behavior of maximum likelihood estimates under nonstandard conditions , 1967 .
[68] L. Hansen. Large Sample Properties of Generalized Method of Moments Estimators , 1982 .
[69] Gebräuchliche Fertigarzneimittel,et al. V , 1893, Therapielexikon Neurologie.