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Stein's formula states that a random variable of the form $z^\top f(z) - \text{div} f(z)$ is mean-zero for functions $f$ with integrable gradient.
D. Dai, P. Rigollet, and T. Zhang, Deviation optimal learning using greedy Q-aggregation , The Annals of Statistics 40
1905
Earlier work this paper cites.
Charles Stein, A bound for the error in the normal approximation to the distribution of a sum of dependent random variables , Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Volume 2: Probability Theory, The Regents of the University of California, 1972
1972
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Colin L Mallows, Some comments on c p , Technometrics 15
1973
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Charles M Stein, Estimation of the mean of a multivariate normal distribution , The annals of Statistics (1981), 1135–1151
1981
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Iain Johnstone, On inadmissibility of some unbiased estimates of loss , Statistical Decision Theory and Related Topics 4
1988
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Ker-Chau Li, Honest confidence regions for nonparametric regression , The Annals of Statistics 17
1989
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Alois Kneip, Ordered linear smoothers , The Annals of Statistics 22
1994
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David L Donoho and Iain M Johnstone, Adapting to unknown smoothness via wavelet shrinkage , Journal of the american statistical association 90
1995
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Aad W Van Der Vaart and Jon A Wellner, Weak convergence , Weak convergence and empirical processes, Springer, 1996, pp. 16–28
1996
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Lawrence C Evans, Partial differential equations and monge-kantorovich mass transfer , Current developments in mathematics 1997
1997
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Vladimir Igorevich Bogachev, Gaussian measures , no. 62, American Mathematical Soc., 1998
1998
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B. Laurent and P. Massart, Adaptive estimation of a quadratic functional by model selection , Ann. Statist. 28
2000
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Gilbert Leung and Andrew R. Barron, Information theory and mixing least-squares regressions , Information Theory, IEEE Transactions on 52
2006
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Nicolai Meinshausen and Peter Bühlmann, High-dimensional graphs and variable selection with the lasso , The annals of statistics 34
2006
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Constantin Niculescu and Lars-Erik Persson, Convex functions and their applications , Springer, 2006
2006
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Joel A Tropp, Just relax: Convex programming methods for identifying sparse signals in noise , IEEE transactions on information theory 52
2006
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Peng Zhao and Bin Yu, On model selection consistency of lasso , Journal of Machine learning research 7
2006
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Cun-Hui Zhang and Jian Huang, The sparsity and bias of the lasso selection in high-dimensional linear regression , Ann. Statist. 36
2008
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Sylvain Arlot and Francis R Bach, Data-driven calibration of linear estimators with minimal penalties , Advances in Neural Information Processing Systems, 2009, pp. 46–54
2009
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Peter J. Bickel, Ya’acov Ritov, and Alexandre B. Tsybakov, Simultaneous analysis of lasso and dantzig selector , Ann. Statist. 37
2009
Cited alongside, same era.
Martin J Wainwright, Sharp thresholds for high-dimensional and noisy sparsity recovery using l1-constrained quadratic programming (lasso) , IEEE transactions on information theory 55
2009
Cited alongside, same era.
Louis HY Chen, Larry Goldstein, and Qi-Man Shao, Normal approximation by stein’s method , Springer Science & Business Media, 2010
2010
Cited alongside, same era.
Michel Talagrand, Mean field models for spin glasses: Volume i: Basic examples , vol. 54, Springer Science & Business Media, 2010
2010
Cited alongside, same era.
Fei Ye and Cun-Hui Zhang, Rate minimaxity of the lasso and dantzig selector for the lq loss in lr balls , Journal of Machine Learning Research 11
2010
Tingni Sun and Cun-Hui Zhang, Sparse matrix inversion with scaled lasso , The Journal of Machine Learning Research 14
2013
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Ryan J Tibshirani, The lasso problem and uniqueness , Electronic Journal of Statistics 7
2013
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Alexandre Belloni, Victor Chernozhukov, and Christian Hansen, Inference on treatment effects after selection among high-dimensional controls , The Review of Economic Studies 81
2014
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Alexandre Belloni, Victor Chernozhukov, and Lie Wang, Pivotal estimation via square-root lasso in nonparametric regression , Ann. Statist. 42
2014
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Sourav Chatterjee, A short survey of stein’s method , arXiv preprint arXiv:1404.1392 (2014)
2014
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Cited alongside, same era.
Cun-Hui Zhang, Nearly unbiased variable selection under minimax concave penalty , The Annals of statistics (2010), 894–942
2010
Cited alongside, same era.
Peter Bühlmann and Sara Van De Geer, Statistics for high-dimensional data: methods, theory and applications , Springer Science & Business Media, 2011
2011
Cited alongside, same era.
Arnak S. Dalalyan and Joseph Salmon, Sharp oracle inequalities for aggregation of affine estimators , The Annals of Statistics 40
2012
Cited alongside, same era.
Christophe Giraud, Sylvie Huet, and Nicolas Verzelen, High-dimensional regression with unknown variance , Statistical Science 27
2012
Cited alongside, same era.
Philippe Rigollet, Kullback–Leibler aggregation and misspecified generalized linear models , Ann. Statist. 40
2012
Cited alongside, same era.
Philippe Rigollet and Alexandre B. Tsybakov, Sparse estimation by exponential weighting , Statist. Sci. 27
2012
Cited alongside, same era.
Ryan J. Tibshirani and Jonathan Taylor, Degrees of freedom in lasso problems , Ann. Statist. 40
2012
Cited alongside, same era.
D. Dai, P. Rigollet, Xia L., and Zhang T., Aggregation of affine estimators , Electon. J. Stat. 8
2014
Later among the works it cites.
Adel Javanmard and Andrea Montanari, Confidence intervals and hypothesis testing for high-dimensional regression , The Journal of Machine Learning Research 15
2014
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2014
Later among the works it cites.
Sara Van de Geer, Peter Bühlmann, Ya’acov Ritov, and Ruben Dezeure, On asymptotically optimal confidence regions and tests for high-dimensional models , The Annals of Statistics 42
2014
Later among the works it cites.
Cun-Hui Zhang and Stephanie S Zhang, Confidence intervals for low dimensional parameters in high dimensional linear models , Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76
2014
Later among the works it cites.
Trevor Hastie, Robert Tibshirani, and Martin Wainwright, Statistical learning with sparsity: the lasso and generalizations , CRC press, 2015
2015
Later among the works it cites.
Murat A Erdogdu, Newton-stein method: an optimization method for glms via stein’s lemma , The Journal of Machine Learning Research 17
2016
Later among the works it cites.
Pierre C Bellec and Alexandre B Tsybakov, Bounds on the prediction error of penalized least squares estimators with convex penalty , Modern Problems of Stochastic Analysis and Statistics, Selected Contributions In Honor of Valentin Konakov (Vladimir Panov, ed.), Springer, 2017
2017
Later among the works it cites.
Pierre C. Bellec, Optimal bounds for aggregation of affine estimators , Ann. Statist. 46
2018
Closest in time.
Pierre C. Bellec, Guillaume Lecué, and Alexandre B. Tsybakov, Slope meets lasso: Improved oracle bounds and optimality , Ann. Statist. 46
2018
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Pierre C Bellec and Cun-Hui Zhang, De-biasing the lasso with degrees-of-freedom adjustment , preprint (2018)
2018
Closest in time.
Ryan J Tibshirani and Saharon Rosset, Excess optimism: How biased is the apparent error of an estimator tuned by sure? , Journal of the American Statistical Association (2018), no. just-accepted
2018
Closest in time.