团队负责人：刘玉坤
研究目标：
面向国家战略和急需,发展大数据分析的基础理论与方法、技术;建立大数据统计学习基础理论与方法,高性能的分布式并行计算算法,以及进行大数据计算与隐私保护协同发展。并把发展的大数据与复杂数据的分析技术与方法应用到数字经济、区块链和金融科技中,解决这些领域的重大基础问题。
代表性论文：
[1] Qin, Jing; Liu, Yukun; Li, Moming; Huang, Chiung-Yu. Distribution-Free Prediction Intervals UnderCovariate Shift, With an Application to Causal inference. Journal of the American Statistical Association.DOl:10.1080/01621459.2024.2356886
[2] Liu, Yukun; Fan, Yan, Biased-sample empirical likelihood weighting for missing data problems: analterative to inverse probability weighting. Journal of the Royal Statistical Society, Series B: StatisticalMethodology . 2023,85(1):67-83.
[3] Chen, Song xi; Qiu, Yumou; Zhang, Shuyi. Sharp optimality for high-dimensional covariance testing.Annals of Statistics. 2023, 51 (5): 1921-1945
[4] Yan, Yibo; Wang, Xiaozhou; Zhang, Riquan. Confidence Intervals and Hypothesis Testing forHigh-dimensional Quantile Regression: Convolution Smoothing and Debiasing. Journal of Machine LearningResearch. 2023,24: 245.
[5] Zhang, Yingying; Wang, Huixia Judy; Zhu, Zhongyi. Single-index Thresholding in Quantile Regression. Journal ofthe American Statistical Association, 2022, 117(540): 2222-2237.