報告題目：Group-orthogonal subsampling for big data linear mixed models
報 告 人：孫法省 教授
所在單位：東北師範大學
報告時間：2022年7月28日 星期四 19:00-20:00
報告地點：騰訊會議：147-322-317
報告摘要：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.
報告人簡介： 孫法省，東北師範大學教授、博導，吉林省優秀教師。博士畢業于南開大學概率論與數理統計專業，分别在加拿大西蒙弗雷澤大學統計與保險系、加州大學洛杉矶分校統計系做訪問學者。主要從事計算機試驗與大數據抽樣與分析方面的研究，研究成果發表在《J Am Stat Assoc》、《Ann Stat》等國際統計學頂級期刊上。曾獲教育部高校科學研究優秀成果獎(科學技術)自然科學獎，全國統計科學研究優秀成果獎、吉林省青年科技獎、吉林省自然科學學術成果獎。
