| A review of distributed statistical inference Y Gao, W Liu, H Wang, X Wang, Y Yan, R Zhang Statistical Theory and Related Fields 6 (2), 89-99, 2022 | 30 | 2022 |
| Automatic, dynamic, and nearly optimal learning rate specification via local quadratic approximation Y Zhu, D Huang, Y Gao, R Wu, Y Chen, B Zhang, H Wang Neural Networks 141, 11-29, 2021 | 7 | 2021 |
| Distributed estimation and inference for spatial autoregression model with large scale networks Y Ren, Z Li, X Zhu, Y Gao, H Wang Journal of Econometrics 238 (2), 105629, 2024 | 1 | 2024 |
| An Asymptotic Analysis of Random Partition Based Minibatch Momentum Methods for Linear Regression Models Y Gao, X Zhu, H Qi, G Li, R Zhang, H Wang Journal of Computational and Graphical Statistics 32 (3), 1083-1096, 2023 | 1 | 2023 |
| Testing Sufficiency for Transfer Learning Z Lin, Y Gao, F Wang, H Wang arXiv preprint arXiv:2304.05636, 2023 | 1 | 2023 |
| A selective review on statistical methods for massive data computation: distributed computing, subsampling, and minibatch techniques X Li, Y Gao, H Chang, D Huang, Y Ma, R Pan, H Qi, F Wang, S Wu, K Xu, ... Statistical Theory and Related Fields, 1-23, 2024 | | 2024 |
| A Latent Factor Model for High-Dimensional Binary Data J Shi, Y Gao, R Pan, H Wang arXiv preprint arXiv:2404.08457, 2024 | | 2024 |
| Mixture Conditional Regression with Ultrahigh Dimensional Text Data for Estimating Extralegal Factor Effects J Shi, F Wang, Y Gao, X Song, H Wang arXiv preprint arXiv:2311.07906, 2023 | | 2023 |
| On the asymptotic properties of a bagging estimator with a massive dataset Y Gao, R Zhang, H Wang Stat 11 (1), e485, 2022 | | 2022 |
| Rejoinder on ‘A review of distributed statistical inference’ Y Gao, W Liu, H Wang, X Wang, Y Yan, R Zhang Statistical Theory and Related Fields 6 (2), 111-113, 2022 | | 2022 |
Hansheng WangGuanghua School of Management, Peking UniversityVerified email at gsm.pku.edu.cn
