报告时间：2019年12月6日 10:00-11:00
报告地点：明德主楼1016
报告题目：Assisted Estimation of Gene Expression Graphical Models
报告摘要：In the study of gene expression data, network analysis has played a uniquely important role. To accommodate the high dimensionality and low sample size and generate interpretable results, regularized estimation is usually conducted in the construction of gene expression Gaussian Graphical Models (GGMs). Gene expressions are regulated by regulators. To better decipher the interconnections among gene expressions, conditional GGMs (cGGMs), which accommodate gene expressions as well as their regulators, have been constructed. In practical data analysis, the construction of both GGMs and cGGMs is often unsatisfactory, mainly caused by the large number of model parameters and limited sample size. In this article, we recognize that, with the regulation between gene expressions and regulators, the sparsity structures of the GGMs and cGGMs satisfy a hierarchy. Accordingly, we propose a joint estimation which reinforces the hierarchical structure and use GGMs to assist the construction of cGGMs and vice versa. Consistency properties are rigorously established, and an effective computational algorithm is developed. In simulation, the assisted construction outperforms the separation construction of GGMs and cGGMs. Two TCGA datasets are analyzed, leading to findings different from the direct competitors. Beyond gene expression data, the proposed approach can be potentially applied to a variety of other high dimensional network analysis.
报告人简介：张庆昭现为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、统计学习等，在JASA、Biometrics、Statistica Sinica、Statistics in Medicine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。