活动时间：2025-07-03 10:40
活动地点：2号学院楼452
主讲人：胡建华
主讲人中文简介：
胡建华，现为上海财经大学数据科学与统计研究院研究员，博士生导师。自2007年以来，为上海财经大学985经济创新平台海归教师，2009年上海市浦江人才获得者。1996年曾任职中南大学副教授。1999年曾访问菲尔兹研究所。2000年获中南大学理学博士学位，2007年获加拿大University of Windsor统计学博士学位。长期从事与统计理论与方法、高维数据分析、多元数据分析、空间计量、投资组合与运输管理等相关的科学研究工作。已主持参加完成包括中国国家自然科学基金（重点或面上）和上海市自然科学基金等在内的科研项目十多项。在包括《Biometrika》、《Bernoulli》、《Statistica Sinica》和《中国科学数学》等在内的国际国内著名学术杂志发表学术论文四十多篇，专著两本。现为国际统计学权威杂志《Journal of Multivariate Analysis》副主编，中国环境统计学会大数据科学分会常务理事，多个国际统计学会会员。
活动内容摘要：
In this talk, we address the statistical problem of response-variable selection with highdimensional response variables and a diverging number of predictor variables with respect to the sample size in the framework of multivariate linear regression. We propose a response best-subset selection model by introducing a 0-1 selection indicator for each response variable, and then develope a response best-subset selector by introducing a separation parameter and a novel penalized least-squares function. The proposed procedure can perform response-variable selection and regression-coefficient estimation simultaneously, and the response best-subset selector has the property of model consistency under mild conditions for both fixed and diverging numbers of predictor variables. Also, consistency and asymptotic normality of regression-coefficient estimators are established for cases with a fixed dimension, and it is found that the Bonferroni test is a special response best-subset selector. Finite-sample simulations show that the response best-subset selector has strong advantages over existing competitors in terms of the Matthews correlation coefficient, a criterion that aims to balance accuracies for both true and false response variables. An analysis of real data demonstrates the effectiveness of the response best-subset selector in an application involving the identification of dosage-sensitive genes.
主持人：童金英