| Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ... Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022 | 338* | 2022 |
| Ensemble forecasts of coronavirus disease 2019 (COVID-19) in the US EL Ray, N Wattanachit, J Niemi, AH Kanji, K House, EY Cramer, J Bracher, ... MedRXiv, 2020.08. 19.20177493, 2020 | 225 | 2020 |
| The united states covid-19 forecast hub dataset EY Cramer, Y Huang, Y Wang, EL Ray, M Cornell, J Bracher, A Brennen, ... Scientific data 9 (1), 462, 2022 | 118 | 2022 |
| Spatiotemporal dynamics, nowcasting and forecasting of COVID-19 in the United States L Wang, G Wang, L Gao, X Li, S Yu, M Kim, Y Wang, Z Gu arXiv preprint arXiv:2004.14103, 2020 | 45 | 2020 |
| Sparse learning and structure identification for ultrahigh-dimensional image-on-scalar regression X Li, L Wang, HJ Wang, Alzheimer’s Disease Neuroimaging Initiative Journal of the American Statistical Association 116 (536), 1994-2008, 2021 | 31 | 2021 |
| Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing G Wang, Z Gu, X Li, S Yu, M Kim, Y Wang, L Gao, L Wang Journal of Applied Statistics 50 (11-12), 2408-2434, 2023 | 22* | 2023 |
| Challenges of COVID-19 Case Forecasting in the US, 2020–2021 VK Lopez, EY Cramer, R Pagano, JM Drake, EB O’Dea, M Adee, T Ayer, ... PLOS Computational Biology 20 (5), e1011200, 2024 | 10 | 2024 |
| Additive partially linear models for ultra‐high‐dimensional regression X Li, L Wang, D Nettleton Stat 8 (1), e223, 2019 | 7 | 2019 |
| Sparse model identification and learning for ultra-high-dimensional additive partially linear models X Li, L Wang, D Nettleton Journal of Multivariate Analysis 173, 204-228, 2019 | 6 | 2019 |
| Nonparametric regression for 3D point cloud Learning X Li, S Yu, Y Wang, G Wang, L Wang, MJ Lai Journal of Machine Learning Research 25 (102), 1-56, 2024 | 4 | 2024 |
| Nonparametric estimation and inference for spatiotemporal epidemic models Y Wang, M Kim, S Yu, X Li, G Wang, L Wang Journal of Nonparametric Statistics 34 (3), 683-705, 2021 | 4 | 2021 |
| Modeling and forecasting COVID-19 L Wang, G Wang, X Li, S Yu, M Kim, Y Wang, Z Gu, L Gao Notices of the American Mathematical Society 68 (4), 585-595, 2021 | 4 | 2021 |
| Comparison of interpolation methods for tropical cyclone track and intensity over Northwestern Pacific basin X Li, W Fang, W Lin Journal of Beijing Normal University (Natural Science) 50 (2), 111, 2014 | 4* | 2014 |
| A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data Z Wang, DB Rowe, X Li, DA Brown Magnetic Resonance Imaging 109, 271-285, 2024 | 1 | 2024 |
| Efficient fully bayesian approach to brain activity mapping with complex-valued fmri data Z Wang, DB Rowe, X Li, DA Brown arXiv preprint arXiv:2310.18536, 2023 | 1 | 2023 |
| Discussion of ‘Multi-scale Fisher’s independence test for multivariate dependence’ D Lee, H El-Zaatari, MR Kosorok, X Li, K Zhang Biometrika 109 (3), 593-596, 2022 | 1 | 2022 |
| Q-Learning Based Methods for Dynamic Treatment Regimes X Li, N Freeman, L Wang Precision Medicine: Methods and Applications, 2023 | | 2023 |
| Functional Individualized Treatment Regimes with Imaging Features X Li, MR Kosorok arXiv preprint arXiv:2304.13003, 2023 | | 2023 |
| A NURBS fitting approach for quality assessment of 3D printing W Fisher, Q Zhang, X Li, X Deng Proceedings of IISE Annual Conference and Expo 2022, 2022 | | 2022 |
| Comment: Diagnostics and Kernel-based Extensions for Linear Mixed Effects Models with Endogenous Covariates H Cho, JP Zitovsky, X Li, M Lu, K Shah, J Sperger, MCB Tsilimigras, ... Statistical Science 35 (3), 396-399, 2020 | | 2020 |
Shan YuUniversity of VirginiaZweryfikowany adres z virginia.edu