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Square-root lasso: pivotal recovery of sparse signals via conic programming

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Cited by:

  1. Quoc Tran-Dinh, 2019. "Proximal alternating penalty algorithms for nonsmooth constrained convex optimization," Computational Optimization and Applications, Springer, vol. 72(1), pages 1-43, January.
  2. Victor Chernozhukov & Christian Hansen & Yuan Liao, 2015. "A lava attack on the recovery of sums of dense and sparse signals," CeMMAP working papers 56/15, Institute for Fiscal Studies.
  3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
  5. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
  6. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
  7. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure," Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 915-958.
  8. Patric Müller & Sara Geer, 2015. "The Partial Linear Model in High Dimensions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 580-608, June.
  9. Simon B chler, Maximilian v. Ehrlich, 2021. "Quantifying Land Use Regulation and its Determinants - Ease of Residential Development across Swiss Municipalities," Diskussionsschriften credresearchpaper32, Universitaet Bern, Departement Volkswirtschaft - CRED.
  10. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
  11. Achim Ahrens & Arnab Bhattacharjee, 2015. "Two-Step Lasso Estimation of the Spatial Weights Matrix," Econometrics, MDPI, vol. 3(1), pages 1-28, March.
  12. Olga Klopp, 2012. "Noisy Low-rank Matrix Completion with General Sampling Distribution," Working Papers 2012-06, Center for Research in Economics and Statistics.
  13. Sophie Brana & Dalila Chenaf-Nicet & Delphine Lahet, 2023. "Drivers of cross-border bank claims: The role of foreign-owned banks in emerging countries," Working Papers 2023.06, International Network for Economic Research - INFER.
  14. A. Belloni & V. Chernozhukov & I. Fernández‐Val & C. Hansen, 2017. "Program Evaluation and Causal Inference With High‐Dimensional Data," Econometrica, Econometric Society, vol. 85, pages 233-298, January.
  15. Mingrui Zhong & Zanhua Yin & Zhichao Wang, 2023. "Variable Selection for Sparse Logistic Regression with Grouped Variables," Mathematics, MDPI, vol. 11(24), pages 1-21, December.
  16. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers 70/13, Institute for Fiscal Studies.
  17. Ismail Shah & Hina Naz & Sajid Ali & Amani Almohaimeed & Showkat Ahmad Lone, 2023. "A New Quantile-Based Approach for LASSO Estimation," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
  18. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
  19. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers CWP57/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  20. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
  21. Kaicheng Chen, 2025. "Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity," Papers 2504.18772, arXiv.org.
  22. Ayed M. Alrashdi & Meshari Alazmi & Masad A. Alrasheedi, 2023. "Generalized Penalized Constrained Regression: Sharp Guarantees in High Dimensions with Noisy Features," Mathematics, MDPI, vol. 11(17), pages 1-27, August.
  23. Guo, Zijian & Kang, Hyunseung & Cai, T. Tony & Small, Dylan S., 2018. "Testing endogeneity with high dimensional covariates," Journal of Econometrics, Elsevier, vol. 207(1), pages 175-187.
  24. Timothy B. Armstrong & Michal Koles'ar & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Papers 2012.14823, arXiv.org, revised Aug 2023.
  25. Anders Bredahl Kock, 2013. "Oracle inequalities for high-dimensional panel data models," CREATES Research Papers 2013-20, Department of Economics and Business Economics, Aarhus University.
  26. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
  27. T. Tony Cai & Zijian Guo & Yin Xia, 2023. "Statistical inference and large-scale multiple testing for high-dimensional regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(4), pages 1135-1171, December.
  28. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers CWP62/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  29. Jana Janková & Rajen D. Shah & Peter Bühlmann & Richard J. Samworth, 2020. "Goodness‐of‐fit testing in high dimensional generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 773-795, July.
  30. van de Geer, Sara, 2016. "Worst possible sub-directions in high-dimensional models," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 248-260.
  31. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers 49/16, Institute for Fiscal Studies.
  32. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 649-688, August.
  33. Lee, Sokbae & Liao, Yuan & Seo, Myung Hwan & Shin, Youngki, 2021. "Sparse HP filter: Finding kinks in the COVID-19 contact rate," Journal of Econometrics, Elsevier, vol. 220(1), pages 158-180.
  34. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
  35. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Central limit theorems and multiplier bootstrap when p is much larger than n," CeMMAP working papers 45/12, Institute for Fiscal Studies.
  36. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020. "lassopack: Model selection and prediction with regularized regression in Stata," Stata Journal, StataCorp LLC, vol. 20(1), pages 176-235, March.
  37. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
  38. Anindya Bhadra & Jyotishka Datta & Nicholas G. Polson & Brandon T. Willard, 2020. "Global-Local Mixtures: A Unifying Framework," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 426-447, August.
  39. Park, Sujeong & Powell, David, 2021. "Is the rise in illicit opioids affecting labor supply and disability claiming rates?," Journal of Health Economics, Elsevier, vol. 76(C).
  40. Laura Freijeiro‐González & Manuel Febrero‐Bande & Wenceslao González‐Manteiga, 2022. "A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates," International Statistical Review, International Statistical Institute, vol. 90(1), pages 118-145, April.
  41. Olga Klopp, 2012. "High Dimensional Matrix Estimation With Unknown Variance Of The Noise," Working Papers 2012-05, Center for Research in Economics and Statistics.
  42. Sardy, Sylvain & Diaz-Rodriguez, Jairo & Giacobino, Caroline, 2022. "Thresholding tests based on affine LASSO to achieve non-asymptotic nominal level and high power under sparse and dense alternatives in high dimension," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
  43. Beyhum, Jad, 2019. "Inference robust to outliers with L1‐norm penalization," TSE Working Papers 19-1032, Toulouse School of Economics (TSE).
  44. Shi, Zhentao, 2016. "Econometric estimation with high-dimensional moment equalities," Journal of Econometrics, Elsevier, vol. 195(1), pages 104-119.
  45. Mohamed Ouhourane & Yi Yang & Andréa L. Benedet & Karim Oualkacha, 2022. "Group penalized quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 495-529, September.
  46. Zhaonan Qu & Yongchan Kwon, 2024. "Distributionally Robust Instrumental Variables Estimation," Papers 2410.15634, arXiv.org, revised Dec 2024.
  47. Corinne Emmenegger & Peter Bühlmann, 2023. "Plug‐in machine learning for partially linear mixed‐effects models with repeated measurements," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(4), pages 1553-1567, December.
  48. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul & Mathieu Rosenbaum & Alexandre B. Tsybakov, 2017. "Pivotal Estimation Via Self-Normalization for High-Dimensional Linear Models with Errors in Variables," Working Papers 2017-26, Center for Research in Economics and Statistics.
  49. Kaixu Yang & Tapabrata Maiti, 2022. "Ultrahigh‐dimensional generalized additive model: Unified theory and methods," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 917-942, September.
  50. Daisuke Ikeda & Mayumi Ojima & Koji Takahashi, 2019. "Financial Interconnectedness, Amplification, and Cross-Border Activity," Bank of Japan Working Paper Series 19-E-11, Bank of Japan.
  51. Jonas Peters & Peter Bühlmann & Nicolai Meinshausen, 2016. "Causal inference by using invariant prediction: identification and confidence intervals," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 947-1012, November.
  52. Xi Chen & Ye Luo & Martin Spindler, 2019. "Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data," Papers 1912.12867, arXiv.org, revised Jan 2020.
  53. Aur'elien Ouattara & Matthieu Bult'e & Wan-Ju Lin & Philipp Scholl & Benedikt Veit & Christos Ziakas & Florian Felice & Julien Virlogeux & George Dikos, 2021. "Scalable Econometrics on Big Data -- The Logistic Regression on Spark," Papers 2106.10341, arXiv.org.
  54. Mehmet Caner & Anders Bredahl Kock, 2016. "Oracle Inequalities for Convex Loss Functions with Nonlinear Targets," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1377-1411, December.
  55. Kock, Anders Bredahl & Callot, Laurent, 2015. "Oracle inequalities for high dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 186(2), pages 325-344.
  56. Jiang, He & Luo, Shihua & Dong, Yao, 2021. "Simultaneous feature selection and clustering based on square root optimization," European Journal of Operational Research, Elsevier, vol. 289(1), pages 214-231.
  57. Fan, Xianqiu & Cheng, Jun & Wang, Hailing & Zhang, Bin & Chen, Zhenzhen, 2024. "A fast trans-lasso algorithm with penalized weighted score function," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
  58. Loann David Denis Desboulets, 2020. "Sparse Manifolds Graphical Modelling with Missing Values: An Application to the Commodity Futures Market," Working Papers hal-02986982, HAL.
  59. Koenker, Roger & Mizera, Ivan, 2014. "Convex Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i05).
  60. Jad Beyhum, 2020. "Inference robust to outliers with L1‐norm penalization," Post-Print hal-03235868, HAL.
  61. Zemin Zheng & Jie Zhang & Yang Li, 2022. "L 0 -Regularized Learning for High-Dimensional Additive Hazards Regression," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2762-2775, September.
  62. Koike, Yuta & Tanoue, Yuta, 2019. "Oracle inequalities for sign constrained generalized linear models," Econometrics and Statistics, Elsevier, vol. 11(C), pages 145-157.
  63. Wagner Piazza Gaglianone & João Victor Issler, 2014. "Microfounded Forecasting," Working Papers Series 372, Central Bank of Brazil, Research Department.
  64. Saulius Jokubaitis & Dmitrij Celov & Remigijus Leipus, 2019. "Sparse structures with LASSO through Principal Components: forecasting GDP components in the short-run," Papers 1906.07992, arXiv.org, revised Oct 2020.
  65. Ben Gillen & Erik Snowberg & Leeat Yariv, 2015. "Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study," NBER Working Papers 21517, National Bureau of Economic Research, Inc.
  66. Umberto Amato & Anestis Antoniadis & Italia De Feis & Irene Gijbels, 2021. "Penalised robust estimators for sparse and high-dimensional linear models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 1-48, March.
  67. Wu, Xiaofei & Ming, Hao & Zhang, Zhimin & Cui, Zhenyu, 2024. "Multi-block alternating direction method of multipliers for ultrahigh dimensional quantile fused regression," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
  68. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
  69. Xiaofei Wu & Rongmei Liang & Hu Yang, 2022. "Penalized and constrained LAD estimation in fixed and high dimension," Statistical Papers, Springer, vol. 63(1), pages 53-95, February.
  70. Wu, Xiaofei & Liang, Rongmei & Zhang, Zhimin & Cui, Zhenyu, 2025. "A unified consensus-based parallel algorithm for high-dimensional regression with combined regularizations," Computational Statistics & Data Analysis, Elsevier, vol. 203(C).
  71. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Estimation of treatment effects with high-dimensional controls," CeMMAP working papers CWP42/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  72. Jokubaitis, Saulius & Celov, Dmitrij & Leipus, Remigijus, 2021. "Sparse structures with LASSO through principal components: Forecasting GDP components in the short-run," International Journal of Forecasting, Elsevier, vol. 37(2), pages 759-776.
  73. Xie, Jichun & Kang, Jian, 2017. "High-dimensional tests for functional networks of brain anatomic regions," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 70-88.
  74. Eric Gautier & Alexandre Tsybakov, 2013. "Pivotal estimation in high-dimensional regression via linear programming," Working Papers hal-00805556, HAL.
  75. Mert Hakan Hekimoğlu & Burak Kazaz, 2020. "Analytics for Wine Futures: Realistic Prices," Production and Operations Management, Production and Operations Management Society, vol. 29(9), pages 2096-2120, September.
  76. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference for High-Dimensional Sparse Econometric Models," Papers 1201.0220, arXiv.org.
  77. Saulius Jokubaitis & Remigijus Leipus, 2022. "Asymptotic Normality in Linear Regression with Approximately Sparse Structure," Mathematics, MDPI, vol. 10(10), pages 1-28, May.
  78. Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2015. "Factorisable sparse tail event curves," SFB 649 Discussion Papers 2015-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  79. Yao Dong & He Jiang, 2018. "A Two-Stage Regularization Method for Variable Selection and Forecasting in High-Order Interaction Model," Complexity, Hindawi, vol. 2018, pages 1-12, November.
  80. Zhang Haixiang & Zheng Yinan & Yoon Grace & Zhang Zhou & Gao Tao & Joyce Brian & Zhang Wei & Schwartz Joel & Vokonas Pantel & Colicino Elena & Baccarelli Andrea & Hou Lifang & Liu Lei, 2017. "Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(3), pages 159-171, August.
  81. Zhu, Ying, 2013. "Sparse Linear Models and Two-Stage Estimation in High-Dimensional Settings with Possibly Many Endogenous Regressors," MPRA Paper 49846, University Library of Munich, Germany.
  82. repec:hum:wpaper:sfb649dp2015-034 is not listed on IDEAS
  83. Sermpinis, Georgios & Tsoukas, Serafeim & Zhang, Ping, 2018. "Modelling market implied ratings using LASSO variable selection techniques," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 19-35.
  84. Wanling Xie & Hu Yang, 2023. "Group sparse recovery via group square-root elastic net and the iterative multivariate thresholding-based algorithm," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(3), pages 469-507, September.
  85. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2021. "Deep Neural Networks for Estimation and Inference," Econometrica, Econometric Society, vol. 89(1), pages 181-213, January.
  86. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
  87. Xie, Fang & Xu, Lihu & Yang, Youcai, 2017. "Lasso for sparse linear regression with exponentially β-mixing errors," Statistics & Probability Letters, Elsevier, vol. 125(C), pages 64-70.
  88. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Demand Estimation with Machine Learning and Model Combination," NBER Working Papers 20955, National Bureau of Economic Research, Inc.
  89. Jacob Bien & Irina Gaynanova & Johannes Lederer & Christian L. Müller, 2019. "Prediction error bounds for linear regression with the TREX," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 451-474, June.
  90. Zemin Zheng & Jinchi Lv & Wei Lin, 2021. "Nonsparse Learning with Latent Variables," Operations Research, INFORMS, vol. 69(1), pages 346-359, January.
  91. Federico Crescenzi, 2023. "Hedonic pricing modelling with unstructured predictors: an application to Italian Fashion Industry," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(4), pages 733-753, December.
  92. Jana Janková & Sara Geer, 2017. "Honest confidence regions and optimality in high-dimensional precision matrix estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 143-162, March.
  93. Pun, Chi Seng & Hadimaja, Matthew Zakharia, 2021. "A self-calibrated direct approach to precision matrix estimation and linear discriminant analysis in high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
  94. Adam Nowak & Patrick Smith, 2015. "Textual Analysis in Real Estate," Working Papers 15-34, Department of Economics, West Virginia University.
  95. Kieran Elmes & Astra Heywood & Zhiyi Huang & Alex Gavryushkin, 2022. "A fast lasso-based method for inferring higher-order interactions," PLOS Computational Biology, Public Library of Science, vol. 18(12), pages 1-23, December.
  96. Quoc Tran-Dinh, 2017. "Adaptive smoothing algorithms for nonsmooth composite convex minimization," Computational Optimization and Applications, Springer, vol. 66(3), pages 425-451, April.
  97. Fan, Jianqing & Feng, Yang & Xia, Lucy, 2020. "A projection-based conditional dependence measure with applications to high-dimensional undirected graphical models," Journal of Econometrics, Elsevier, vol. 218(1), pages 119-139.
  98. Barbara Guardabascio & Filippo Moauro & Luke Mosley, 2024. "Indirect estimation of the monthly transport turnover indicator in Italy," Empirical Economics, Springer, vol. 67(2), pages 531-566, August.
  99. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.
  100. Alexander Jaax & Annabelle Mourougane & Frederic Gonzales, 2024. "Nowcasting services trade for the G7 economies," The World Economy, Wiley Blackwell, vol. 47(4), pages 1336-1386, April.
  101. Zanhua Yin, 2020. "Variable selection for sparse logistic regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(7), pages 821-836, October.
  102. Wang, Lie, 2013. "The L1 penalized LAD estimator for high dimensional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 135-151.
  103. Alexis Derumigny, 2017. "Improved bounds for Square-Root Lasso and Square-Root Slope," Working Papers 2017-53, Center for Research in Economics and Statistics.
  104. Tianxi Cai & T. Tony Cai & Zijian Guo, 2021. "Optimal statistical inference for individualized treatment effects in high‐dimensional models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 669-719, September.
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