报告题目：A Generalized Knockoff Procedure for FDR Control in Structural Change Detection

报 告 人：刘婧媛教授（厦门大学）

报告时间：2022年6月9日  9:00-11:00

报告地点：腾讯会议（859587458，密码：0609）

报告摘要：

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，教育部青年长江学者。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。