报告题目:Conditional Score Matching for High-Dimensional Partial Graphical Models
时间:2020年1月3日(周五)下午3:00-4:00
地点:藕舫楼818室
报告人:张庆昭 副教授
主持人:来 鹏 副教授
报告摘要:Network construction has been heavily exploited in multivariate data analysis. In many cases, connections between a large portion of variables are of minimal importance. As such, partial graphs have played an important role in network construction. Due to the existence of a multiplicative normalization constant, the existing construction approaches may bear high computational cost. In this paper, we propose conditional score matching for high-dimensional partial graphical models. This approach is uniquely advantageous by being not influenced by the multiplicative normalization constant. Effective computational algorithm is developed, and it is shown that computational complexity of the proposed method is less than that of those in the literature. Statistical properties are established, and two extensions are explored to incorporate more information and accommodate more general distributions. A wide spectrum of simulations and the analysis of a breast cancer gene expression dataset demonstrate competitive performance of the proposed methods.
报告人简介:张庆昭, 厦门大学经济学院统计系和王亚南经济研究院副教授、博士生导师。2013年获得中国科学院数学与系统科学研究院概率论与数理统计博士学位,先后在中国科学院大学和美国耶鲁大学进行博士后研究。主要研究方向为高维数据分析、多源数据融合、函数数据分析、统计学习和数据挖掘等,在JASA、Biometrics、Statistica Sinica等期刊发表论文30余篇。国际统计学会推选会员,主持国家自科面上、青年各1项,教育部基金1项。
数学与统计学院
2019年12月30日