| An effective intrusion detection framework based on SVM with feature augmentation H Wang, J Gu, S Wang Knowledge-Based Systems 136, 130-139, 2017 | 296 | 2017 |
| A novel approach to intrusion detection using SVM ensemble with feature augmentation SW Jie Gu,Lihong Wang, Huiwen Wang Computers & Security 86, 53-62, 2019 | 176 | 2019 |
| Semiparametric regression analysis of clustered survival data with semi-competing risks M Peng, L Xiang, S Wang Computational Statistics & Data Analysis 124, 53-70, 2018 | 53 | 2018 |
| The STIRPAT analysis on carbon emission in Chinese cities: An asymmetric laplace distribution mixture model S Wang, T Zhao, H Zheng, J Hu Sustainability 9 (12), 2237, 2017 | 45 | 2017 |
| Statistical regression modeling for energy consumption in wastewater treatment Y Yu, Z Zou, S Wang Journal of Environmental Sciences 75, 201-208, 2019 | 42 | 2019 |
| Examining Determinants of CO2 Emissions in 73 Cities in China H Zheng, J Hu, R Guan, S Wang Sustainability 8 (12), 1296, 2016 | 42 | 2016 |
| Examining the influencing factors of CO2 emissions at city level via panel quantile regression: evidence from 102 Chinese cities HW Haitao Zheng, Jie Hu, Shanshan Wang Applied Economics 51 (35), 3906-3919, 2019 | 36 | 2019 |
| A robust spatial autoregressive scalar-on-function regression with t-distribution T Huang, G Saporta, H Wang, S Wang Advances in Data Analysis and Classification 15, 57-81, 2021 | 21* | 2021 |
| Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19 H Wang, Y Zhang, S Lu, S Wang F1000Research 9, 2020 | 19 | 2020 |
| Interval-valued data regression using partial linear model Y Wei, S Wang, H Wang Journal of Statistical Computation and Simulation 87 (16), 3175-3194, 2017 | 16 | 2017 |
| Spatial partial least squares autoregression: Algorithm and applications H Wang, J Gu, S Wang, G Saporta Chemometrics and Intelligent Laboratory Systems 184, 123-131, 2019 | 15 | 2019 |
| Functional variable selection via Gram–Schmidt orthogonalization for multiple functional linear regression R Liu, H Wang, S Wang Journal of statistical computation and simulation 88 (18), 3664-3680, 2018 | 12 | 2018 |
| Penalized empirical likelihood inference for sparse additive hazards regression with a diverging number of covariates S Wang, L Xiang Statistics and Computing 27, 1347-1364, 2017 | 11 | 2017 |
| Convex clustering method for compositional data via sparse group lasso X Wang, H Wang, S Wang, J Yuan Neurocomputing 425, 23-36, 2021 | 10 | 2021 |
| Forecasting open-high-low-close data contained in candlestick chart H Wang, W Huang, S Wang arXiv preprint arXiv:2104.00581, 2021 | 9 | 2021 |
| Linear mixed-effects model for multivariate longitudinal compositional data Z Wang, H Wang, S Wang Neurocomputing 335, 48-58, 2019 | 8 | 2019 |
| A pseudo principal component analysis method for multi-dimensional open-high-low-close data in candlestick chart W Huang, H Wang, S Wang Communications in Statistics-Theory and Methods 53 (10), 3472-3498, 2024 | 7 | 2024 |
| Robust regression for interval-valued data based on midpoints and log-ranges Q Zhao, H Wang, S Wang Advances in Data Analysis and Classification 17 (3), 583-621, 2023 | 7 | 2023 |
| Sliced inverse regression method for multivariate compositional data modeling H Wang, Z Wang, S Wang Statistical Papers 62, 361-393, 2021 | 7 | 2021 |
| Linear mixed-effects model for longitudinal complex data with diversified characteristics Z Wang, H Wang, S Wang, S Lu, G Saporta Journal of Management Science and Engineering 5 (2), 105-124, 2020 | 7 | 2020 |
Haitao ZhengSchool of Economics and Management,Beihang University在 buaa.edu.cn 的电子邮件经过验证
