报告题目：Group-orthogonal subsampling for big data linear mixed models
报 告 人：孙法省 教授
所在单位：东北师范大学
报告时间：2022年7月28日 星期四 19:00-20:00
报告地点：腾讯会议：147-322-317
报告摘要：Linear mixed model is a popular and common modeling method in statistical analysis. It is computationally difficult to obtain parameter estimates in linear mixed model for big data. The current subsampling methods are mainly aimed at the situation where the data is independent, without considering the correlation within the data. We provide some theoretical results on information matrix for linear mixed model. Based on these findings, an optimal subsampling method for linear mixed model is proposed, which maximizes the determinant of the variance-covariance matrix of the subsampling estimator. Besides, the proposed subsampling procedure is also optimal under A-optimality criterion, which minimizes the trace of the variance-covariance matrix of the subsampling estimator. Furthermore, asymptotic property of the subsampling estimator is established. Numerical examples based on both simulated and real data are provided to illustrate the proposed subsampling method.
报告人简介： 孙法省，东北师范大学教授、博导，吉林省优秀教师。博士毕业于南开大学概率论与数理统计专业，分别在加拿大西蒙弗雷泽大学统计与保险系、加州大学洛杉矶分校统计系做访问学者。主要从事计算机试验与大数据抽样与分析方面的研究，研究成果发表在《J Am Stat Assoc》、《Ann Stat》等国际统计学顶级期刊上。曾获教育部高校科学研究优秀成果奖(科学技术)自然科学奖，全国统计科学研究优秀成果奖、吉林省青年科技奖、吉林省自然科学学术成果奖。
