| Enhancement of detecting permanent water and temporary water in flood disasters by fusing sentinel-1 and sentinel-2 imagery using deep learning algorithms: Demonstration of … Y Bai, W Wu, Z Yang, J Yu, B Zhao, X Liu, H Yang, E Mas, S Koshimura Remote Sensing 13 (11), 2220, 2021 | 48 | 2021 |
| Jackknife empirical likelihood inference for the mean absolute deviation Y Zhao, X Meng, H Yang Computational statistics & data analysis 91, 92-101, 2015 | 40 | 2015 |
| Two-way partial AUC and its properties H Yang, K Lu, X Lyu, F Hu Statistical methods in medical research 28 (1), 184-195, 2019 | 38 | 2019 |
| Smoothed jackknife empirical likelihood inference for the difference of ROC curves H Yang, Y Zhao Journal of Multivariate Analysis 115, 270-284, 2013 | 36 | 2013 |
| Large scale GPS trajectory generation using map based on two stage GAN X Wang, X Liu, Z Lu, H Yang Journal of Data Science 19 (1), 126-141, 2021 | 35 | 2021 |
| Smoothed jackknife empirical likelihood inference for ROC curves with missing data H Yang, Y Zhao Journal of Multivariate Analysis 140, 123-138, 2015 | 30 | 2015 |
| Nightlight as a proxy of economic indicators: Fine-grained GDP inference around Chinese mainland via attention-augmented CNN from daytime satellite imagery H Liu, X He, Y Bai, X Liu, Y Wu, Y Zhao, H Yang Remote Sensing 13 (11), 2067, 2021 | 23 | 2021 |
| A nonparametric approach for partial areas under ROC curves and ordinal dominance curves H Yang, K Lu, Y Zhao Statistica Sinica, 357-371, 2017 | 21 | 2017 |
| Mtrec: Multi-task learning over bert for news recommendation Q Bi, J Li, L Shang, X Jiang, Q Liu, H Yang Findings of the Association for Computational Linguistics: ACL 2022, 2663-2669, 2022 | 19 | 2022 |
| Technical solution discussion for key challenges of operational convolutional neural network-based building-damage assessment from satellite imagery: Perspective from benchmark … J Su, Y Bai, X Wang, D Lu, B Zhao, H Yang, E Mas, S Koshimura Remote Sensing 12 (22), 3808, 2020 | 16 | 2020 |
| Smoothed jackknife empirical likelihood for the one-sample difference of quantiles H Yang, Y Zhao Computational Statistics & Data Analysis 120, 58-69, 2018 | 16 | 2018 |
| Smoothed jackknife empirical likelihood for the difference of two quantiles H Yang, Y Zhao Annals of the Institute of Statistical Mathematics 69, 1059-1073, 2017 | 14 | 2017 |
| Smoothed empirical likelihood for ROC curves with censored data H Yang, Y Zhao Journal of Multivariate Analysis 109, 254-263, 2012 | 13 | 2012 |
| New empirical likelihood inference for linear transformation models H Yang, Y Zhao Journal of Statistical Planning and Inference 142 (7), 1659-1668, 2012 | 11 | 2012 |
| Boosted histogram transform for regression Y Cai, H Hang, H Yang, Z Lin International Conference on Machine Learning, 1251-1261, 2020 | 10 | 2020 |
| Under-bagging nearest neighbors for imbalanced classification H Hang, Y Cai, H Yang, Z Lin Journal of Machine Learning Research 23 (118), 1-63, 2022 | 9 | 2022 |
| Smoothed empirical likelihood inference for the difference of two quantiles with right censoring H Yang, C Yau, Y Zhao Journal of Statistical Planning and Inference 146, 95-101, 2014 | 9 | 2014 |
| Jackknife empirical likelihood for linear transformation models with right censoring H Yang, S Liu, Y Zhao Annals of the Institute of Statistical Mathematics 68, 1095-1109, 2016 | 8 | 2016 |
| Jackknife empirical likelihood inference for the Pietra ratio Y Zhao, Y Su, H Yang Computational Statistics & Data Analysis 152, 107049, 2020 | 5 | 2020 |
| A varying-coefficient regression approach to modeling the effects of wind speed on the dispersion of pollutants K He, Y Wang, W Su, H Yang Environmental and Ecological Statistics 29 (2), 433-452, 2022 | 4 | 2022 |
