報 告 人：蔣杭進 研究員
報告題目：Two-sample Distribution Comparison and Its Applications
報告時間：2023年07月24日（周一）上午10：00
報告地點：靜遠樓1506會議室
主辦單位：數學與統計學院、數學研究院、科學技術研究院
報告人簡介：
蔣杭進，香港中文大學統計學博士，現任浙江大學“百人計劃”研究員，博導，浙江省生物信息學學會理事、副秘書長。2022年入選省級青年人才計劃。2023年成為教育部生物學科”101計劃”生物信息學核心課程建設專家。他曾訪問香港理工大學應用數學系和斯坦福大學統計系Wing Hung Wong 教授。目前, 他主要致力于以統計方法推動生命科學的發展, 為分析生物醫學數據提出新方法。研究興趣為貝葉斯數據分析、生物統計/計算生物學、統計推斷和深度學習。目前，他已經在Nature Astronomy, The Astrophysical Journal, Statistica Sinica, Methods, PLoS Computational Biology, Briefings in Bioinformatics, IEEE-TCBB, The Plant Journal,和Advanced Biology等生物學、統計學和天文學領域的高水平期刊上發表論文20余篇。主持國家自然科學基金青年項目、浙江省人民醫院委托項目一項和浙江省農科院委托項目一項，參與國家自然科學基金面上項目多項、浙江省重大研發計劃( 排名第2)一項和中國煙草總公司重大科技計劃(子課題負責人)。
報告摘要：
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.