朱学虎 |
1.统计学习;
2.高维数据分析
3.金融统计
4.统计
1.主持国家社会科学基金一般项目(项目名称:模型自适应合成检验方法及其应用研究,No.21BTJ048,2021.06-2024.06)。
2. 主持国家自然科学基金重点项目的子课题(项目名称:因果分析的若干统计学基础问题的研究及其应用,No.2131006,2022.01-2026.12.)
3.主持国家重点研发计划项目的子任务(项目名称:数据与机理融合的大数据统计推断,No.2022YFA1003803,2022.12—2027.11月)
1.陕西高校第五批“青年杰出人才支持计划”;
2.2021年获得“仲英青年学者”;
3.2018年获得陕西省数学会青年教师优秀论文一等奖;
十篇代表性论文:
1.Xuehu Zhu, Qiming Zhang, Jun Zhang, Lixing Zhu* and Yuoyao Yu. (2022). Specification testing of regression models with mixed discrete and continuous predictors. Journal of Business & Economic Statistics, 1-39.
2.Lingzhu Li#, Xuehu Zhu#, and Lixing Zhu*. (2023). Adaptive-to-model hybrid of tests for regressions. Journal of the American Statistical Association, 118 (541), 514-523.(共同一作)
3.Xuehu Zhu, Jian Guo, Xu Guo, Lixing Zhu and Jiaseng Zheng. (2023). Bandwidth selection for large covariance and precision matrices, Statistica Sinica, accepted.
4.Xuehu Zhu, Luoyao Yu, Jiaqi Huang, Junmin Liu and Lixing Zhu. Moment deviation subspaces of dimension reduction for high-dimensional data with change structure. (2023). Statistica Sinica, accepted.
5.Wenbiao Zhao, Xuehu Zhu and Lixing Zhu. Detecting multiple change points: the PULSE criterion, Statistics Sinica, (2023), 33, 431-451
6.Xuehu Zhu, Jun Lu, Jun Zhang and Lixing Zhu*. (2021). Testing for conditional independence: a groupwise dimension reduction-based adaptive-to-model approach. Scandinavian Journal of Statistics, 48, 549-405.
7.Xuehu Zhu, Yue Kang and Junmin Liu* (2020). Estimation of virtual dimensionality via thresholding ridge ratio criterion. IEEE Transactions on Geoscience and Remote Sensing, 58, 637-649.
8.Falong Tan, Xuehu Zhu and Lixing Zhu * (2018). A projection-based adaptive-to-model test for regressions. Statistica Sinica,28,157-188.
9.Xuehu Zhu, Xu Guo and Lixing Zhu.*(2017) An adaptive-to-model test for partially parametric single-index models. Statisticas and Computing,27,1193-1204.
10.Xuehu Zhu, Guo Xu, Tao Wang and Lixing Zhu*. Dimensionality determination: a thresholding double ridge ratio criterion, Computational Statistics & Data Analysis, (2020), 146, 106910.