时间：2022年5月18日，星期三，上午
报告一(9:30---10:15)
题目：A New Projection Test for Mean Vector in High Dimensions
摘要：This paper studies the projection test for high-dimensional mean vectors via optimal projection. The idea of projection test is to project high-dimensional data onto a space of low dimension such that traditional methods can be applied. We propose a new estimation for the optimal projection direction by solving a constrained and regularized quadratic programming. The test is constructed using the estimated optimal projection direction. It is based on a data-splitting procedure, which achieves an exact t-test under normality assumption. To mitigate the power loss due to data-splitting, we further propose an online framework, which iteratively updates the estimation of projection direction when new observations arrive. Various simulation studies as well as a real data example show that the proposed online-style projection test retains the type I error rate well and is more powerful than other existing tests.
报告人简介：钟威，现任厦门大学王亚南经济研究院和经济学院统计系教授、博士生导师。2012年获得美国宾夕法尼亚州立大学统计学博士学位，2014年和2017年分别破格晋升副教授和教授，2018年入选厦门大学“南强青年拔尖人才”（A类），国家自然科学基金优秀青年基金获得者（2019），福建省杰出青年基金获得者（2019）。主要从事高维数据统计分析、统计学习算法、计量经济学、统计学和数据科学的应用等研究。担任美国统计协会(ASA)期刊《Statistical Analysis and Data Mining》和加拿大统计学会期刊《Canadian Journal of Statistics》的AE，在The Annals of Statistics, Journal of the American Statistical Association, Biometrika, Journal of Econometrics, Journal of Business & Economic Statistics, Biometrics, Annals of Applied Statistics, Statistica Sinica，中国科学数学等国内外统计学权威期刊发表（含接收）20多篇论文，其中入选ESI前1%高被引论文2篇。主要讲授《数理统计》、《广义线性模型》、《计量经济学》、《统计数据分析》等本科和研究生课程，多次获得学院教学优秀奖，2016年获得厦门大学第五届英语教学比赛一等奖，2020年获得厦门大学第十五届青年教师技能比赛特等奖,2021年获得厦门大学教学创新大赛一等奖，2021年获得福建省“向上向善好青年”称号。个人主页：https://wise.xmu.edu.cn/people/faculty/bd5bc78c-99d3-46b0-873d-32fa79a278f5.html
报告二(10:20---11:05)
题目：A Generalized Knockoff Procedure for FDR Control in Structural Change Detection
摘要：Controlling false discovery rate (FDR) is crucial for variable selection, multiple testing, among other signal detection problems. In literature, there is certainly no shortage of FDR control strategies when selecting individual features. Yet lack of relevant work has been done regarding structural change detection, including, but not limited to change point identification, profile analysis for piecewise constant coefficients, and integration analysis with multiple data sources. In this paper, we propose a generalized knockoff procedure (GKnockoff) for FDR control under such problem settings. We prove that the GKnockoff possesses pairwise exchangeability, and is capable of controlling the exact FDR under finite sample sizes. We further explore GKnockoff under high dimensionality, by first introducing a new screening method to filter the high-dimensional potential structural changes. We adopt a data splitting technique to first reduce the dimensionality via screening and then conduct GKnockoff on the refined selection set. Numerical comparisons with other methods show the superior performance of GKnockoff, in terms of both FDR control and power. We also implement the proposed method to analyze a macroeconomic dataset for detecting change points in the consumer price index, as well as the unemployment rate.
报告人简介：刘婧媛，厦门大学经济学院统计学与数据科学系、王亚南经济研究院教授、博士生导师，2021年入选国家级人才计划。2013年博士毕业于美国宾夕法尼亚州立大学统计学专业。科研方面主要从事高维数据分析、交叉学科的统计方法研究、统计基因学等领域的工作，在JASA，JOE, JBES, Annals of Applied Statistics等国际权威学术期刊发表论文20余篇；主持国家自然科学基金面上项目、青年项目等国家级、省部级多项科研项目；2018年入选福建省杰出青年科研人才培育计划。教学方面曾获厦门大学教学比赛特等奖、福建省一流课程等荣誉。
个人主页：https://stats.xmu.edu.cn/info/1020/1055.htm
会议信息：
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会议主题：统计学系列学术报告
会议时间：2022/05/18 09:00-11:30 (GMT+08:00)中国标准时间-北京
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