时间：2022年12月16日上午

会议ID：699 751 968

报告人介绍：刘妍岩 武汉大学数学与统计学院教授，博士生导师。2001年获武汉大学理学博士学位。主要研究方向为生存分析、半参数统计推断、复杂高维数据模型结构选择以及大数据统计分析技术等。曾到美国北卡来罗纳大学教堂山分校、加拿大Simon-Fraser大学、香港理工大学、香港中文大学、德国Greifswald大学等学校短期访问和工作。主持完成国家自然科学基金以及教育部基金项目6项，目前主持国家自然科学基金面上项目一项。在统计学期刊 Journalof Machine Learning Research, Biometrics, Biostatistics, Genetics，Lifetime DataAnalysis等期刊发表SCI研究论文六十余篇。目前担任statistical papers 副主编，以及中国现场统计学会第十一届理事会常务理事、中国数学会女专家工作委员会委员。

Title：

Distributed inference for two-sample U-statistics in massive data analysis

Abstract：

This paper considers distributed inference for two-sample U-statistics under the massive data setting. In order to reduce the computational complexity, this paper proposes distributed two-sample U-statistics and block-wise linear two sample U-statistics. The block-wise linear two-sample U-statistic, which requires less communication cost, is more computationally efficient especially when the data are stored in different locations. The asymptotic properties of both types of distributed two-sample U-statistics are established. In addition, this paper proposes bootstrap algorithms to approximate the distributions of distributed two-sample U-statistics and block-wise linear two-sample U-statistics for both non-degenerate and degenerate cases. The distributed weighted bootstrap for the distributed two-sample U-statistic is new in the literature. The proposed bootstrap procedures are computationally efficient and are suitable for distributed computing platforms with theoretical guarantees. Extensive numerical studies illustrate that the proposed distributed approaches are feasible and effective.