报 告 人：蒋杭进 研究员
报告题目：Two-sample Distribution Comparison and Its Applications
报告时间：2023年07月24日（周一）上午10：00
报告地点：静远楼1506会议室
主办单位：数学与统计学院、数学研究院、科学技术研究院
报告人简介：
蒋杭进，香港中文大学统计学博士，现任浙江大学“百人计划”研究员，博导，浙江省生物信息学学会理事、副秘书长。2022年入选省级青年人才计划。2023年成为教育部生物学科”101计划”生物信息学核心课程建设专家。他曾访问香港理工大学应用数学系和斯坦福大学统计系Wing Hung Wong 教授。目前, 他主要致力于以统计方法推动生命科学的发展, 为分析生物医学数据提出新方法。研究兴趣为贝叶斯数据分析、生物统计/计算生物学、统计推断和深度学习。目前，他已经在Nature Astronomy, The Astrophysical Journal, Statistica Sinica, Methods, PLoS Computational Biology, Briefings in Bioinformatics, IEEE-TCBB, The Plant Journal,和Advanced Biology等生物学、统计学和天文学领域的高水平期刊上发表论文20余篇。主持国家自然科学基金青年项目、浙江省人民医院委托项目一项和浙江省农科院委托项目一项，参与国家自然科学基金面上项目多项、浙江省重大研发计划( 排名第2)一项和中国烟草总公司重大科技计划(子课题负责人)。
报告摘要：
We proposed nonparametric test statistics for the problem of the two-sample distribution comparison. These test statistics combine the merits of the chi-squared and Kolmogorov–Smirnov statistics, and provide new insights into the equality test of the unspecified distributions underlying the two independent samples. Based on our new statistics, we propose a marginal screening procedure and a pairwise joint screening procedure for detecting important variables in high-dimensional binary classification. Both screening procedures have the consistent screening property, which is stronger than the sure screening property of most existing methods. The marginal screening procedure is much more powerful than other methods over a broad range of cases, and the pairwise joint screening procedure provides a way of detecting variables with a joint effect, but no marginal effect. Extensive simulations and a real-data application show the effectiveness and advantages of the proposed methods.