Penalized composite quasi-likelihood for ultrahigh dimensional variable selection J Bradic, J Fan, W Wang Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2011 | 202 | 2011 |
Regularization for Cox’s proportional hazards model with NP-dimensionality J Bradic, J Fan, J Jiang Annals of statistics 39 (6), 3092, 2011 | 178 | 2011 |
Linear Hypothesis Testing in Dense High-Dimensional Linear Models Y Zhu, J Bradic Journal of the American Statistical Association: Theory and Methods 113 (524 …, 2018 | 94 | 2018 |
Cultivating disaster donors: A case application of scalable analytics on massive data IO Ryzhov, B Han, J Bradic Management Science, 2013 | 56* | 2013 |
A Tuning-free Robust and Efficient Approach to High-dimensional Regression (with a discussion) L Wang, B Peng, J Bradic, R Li, Y Wu Journal of the American Statistical Association: Theory and Methods, 2020 | 52* | 2020 |
Sparsity Double Robust Inference of Average Treatment Effects J Bradic, S Wager, Y Zhu https://arxiv.org/abs/1905.00744, 2019 | 51 | 2019 |
Significance testing in non-sparse high-dimensional linear models Y Zhu, J Bradic Electronic Journal of Statistics 12 (2), 3312-3364, 2018 | 51* | 2018 |
Confidence intervals for high-dimensional Cox model Y Yu, J Bradic, RJ Samworth Statistica Sinica, 2018 | 44 | 2018 |
Boosting in the presence of outliers: adaptive classification with non-convex loss functions AH Li, J Bradic Journal of the American Statistical Association: Theory and Methods 113 (522 …, 2018 | 40 | 2018 |
High-dimensional semi-supervised learning: in search for optimal inference of the mean Y Zhang, J Bradic https://arxiv.org/abs/1902.00772, 2019 | 37 | 2019 |
Uniform inference for high-dimensional quantile regression: linear functionals and regression rank scores J Bradic, M Kolar arXiv preprint arXiv:1702.06209, 55, 2017 | 29 | 2017 |
Fair Policy Targeting D Viviano, J Bradic arXiv preprint arXiv: 2005.12395, 2020 | 27* | 2020 |
Testability of high-dimensional linear models with non-sparse structures J Bradic, J Fan, Y Zhu https://arxiv.org/abs/1802.09117, 2018 | 27* | 2018 |
Synthetic learner: model-free inference on treatments over time D Viviano, J Bradic https://arxiv.org/abs/1904.01490, 2019 | 23* | 2019 |
Robustness in sparse high-dimensional linear models: Relative efficiency and robust approximate message passing J Bradic | 23* | 2016 |
Minimax semiparametric learning with approximate sparsity J Bradic, V Chernozhukov, WK Newey, Y Zhu arXiv preprint arXiv:1912.12213, 2019 | 21 | 2019 |
Censored quantile regression forests AH Li, J Bradic https://arxiv.org/abs/1902.03327, 2019 | 21 | 2019 |
Randomized maximum-contrast selection: subagging for large-scale regression J Bradic Electronic Journal of Statistics 10 (1), 121-170, 2016 | 20* | 2016 |
Fixed effects testing in high-dimensional linear mixed models J Bradic, G Claeskens, T Gueuning Journal of the American Statistical Association: Theory & Methods, 2017 | 19 | 2017 |
Structured estimation for the nonparametric Cox model J Bradic, R Song | 18* | 2015 |