报告人：许王莉 教授
报告题目：Differentially Private Joint Independence Test
报告时间：2026年5月21日（周四）下午3：00
报告地点：云龙校区6号楼304报告厅
主办单位：数学与统计学院、数学研究院、科学技术研究院
报告人简介：
许王莉：2006年毕业于中国科学院数学与系统科学研究院应用所概率论与数理统计专业，目前是中国现场统计研究会生存分析分会副秘书长、国际生物统计学会中国分（IBS-CHINA）青年理事，众多国内外统计学术期刊的审稿专家，现任中国人民大学书院建设与管理中心副主任、明理书院副院长、统计学教授。近年来一直从事模型拟合优度检验，高维数据分析，随机缺失数据，两阶段抽样数据以及纵向数据分析等方面的统计推断研究。先后承担了“新世纪优秀人才计划”，“北京市科技新星计划”，国家自然科学面上基金，国家自然科学青年基金和教育部人文社科基金等多项科研课题，在统计学国际一流期刊发表论文40余篇，并在科学出版社合作出版《非参数蒙特卡洛检验及其应用》和单著《缺失数据的模型检验及其应用》。
报告摘要：
Identification of joint dependence among more than two random vectors plays an important role in many statistical applications, where the data may contain sensitive or confidential information. In this paper, we consider the d-variable Hilbert-Schmidt independence criterion (dHSIC) in the context of differential privacy. Given that the limiting distribution of the empirical estimate of dHSIC is a complicated Gaussian chaos, constructing tests in the non-private regime is typically based on permutation and bootstrap methods. To detect joint dependence in privacy, we propose a dHSIC-based testing procedure by employing a differentially private permutation methodology. Our method enjoys privacy guarantee, valid level and pointwise consistency, while the bootstrap counterpart suffers from inconsistent power. We further investigate the uniform power of the proposed test in dHSIC metric and L2 metric, indicating that the proposed test attains the minimax optimal power across different privacy regimes. As a byproduct, our results also establish the pointwise and uniform power of the non-private permutation dHSIC, addressing an unsolved question remained in Pfister(2018). Both numerical simulations and real data analysis in causal inference suggest our proposed test performs well empirically.