| A general theory of hypothesis tests and confidence regions for sparse high dimensional models Y Ning, H Liu | 435 | 2017 |
| Glycated hemoglobin and the risk of kidney disease and retinopathy in adults with and without diabetes E Selvin, Y Ning, MW Steffes, LD Bash, R Klein, TY Wong, BC Astor, ... diabetes 60 (1), 298-305, 2011 | 178 | 2011 |
| High dimensional semiparametric latent graphical model for mixed data J Fan, H Liu, Y Ning, H Zou Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2017 | 156 | 2017 |
| Robust estimation of causal effects via a high-dimensional covariate balancing propensity score Y Ning, P Sida, K Imai Biometrika 107 (3), 533-554, 2020 | 112 | 2020 |
| Heterogeneity-aware and communication-efficient distributed statistical inference R Duan, Y Ning, Y Chen Biometrika 109 (1), 67-83, 2022 | 108 | 2022 |
| A unified theory of confidence regions and testing for high-dimensional estimating equations M Neykov, Y Ning, JS Liu, H Liu | 103 | 2018 |
| High dimensional em algorithm: Statistical optimization and asymptotic normality Z Wang, Q Gu, Y Ning, H Liu Advances in neural information processing systems 28, 2015 | 101 | 2015 |
| Testing and confidence intervals for high dimensional proportional hazards models EX Fang, Y Ning, H Liu Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2017 | 100 | 2017 |
| Efficient augmentation and relaxation learning for individualized treatment rules using observational data YQ Zhao, EB Laber, Y Ning, S Saha, BE Sands Journal of Machine Learning Research 20 (48), 1-23, 2019 | 93 | 2019 |
| Improving covariate balancing propensity score: A doubly robust and efficient approach J Fan, K Imai, H Liu, Y Ning, X Yang URL: https://imai. fas. harvard. edu/research/CBPStheory. html, 2016 | 77 | 2016 |
| High dimensional expectation-maximization algorithm: Statistical optimization and asymptotic normality Z Wang, Q Gu, Y Ning, H Liu arXiv preprint arXiv:1412.8729, 2014 | 60 | 2014 |
| Differential principal component analysis of ChIP-seq H Ji, X Li, Q Wang, Y Ning Proceedings of the National Academy of Sciences 110 (17), 6789-6794, 2013 | 59 | 2013 |
| Adaptive estimation in structured factor models with applications to overlapping clustering X Bing, F Bunea, Y Ning, M Wegkamp | 56 | 2020 |
| Optimal sampling for generalized linear models under measurement constraints T Zhang, Y Ning, D Ruppert Journal of Computational and Graphical Statistics 30 (1), 106-114, 2021 | 53 | 2021 |
| Optimal covariate balancing conditions in propensity score estimation J Fan, K Imai, I Lee, H Liu, Y Ning, X Yang Journal of Business & Economic Statistics 41 (1), 97-110, 2022 | 52 | 2022 |
| On semiparametric exponential family graphical models Z Yang, Y Ning, H Liu arXiv preprint arXiv:1412.8697, 2014 | 52* | 2014 |
| A likelihood ratio framework for high-dimensional semiparametric regression Y Ning, T Zhao, H Liu | 48 | 2017 |
| Test of significance for high-dimensional longitudinal data EX Fang, Y Ning, R Li Annals of statistics 48 (5), 2622, 2020 | 47 | 2020 |
| High-dimensional mixed graphical model with ordinal data: Parameter estimation and statistical inference H Feng, Y Ning The 22nd international conference on artificial intelligence and statistics …, 2019 | 34 | 2019 |
| Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data J Zhao, Y Yang, Y Ning Statistica Sinica 28 (4), 2125-2148, 2018 | 30 | 2018 |
