时 间：2022/06/09 9:00—11:00

腾讯会议：859-587-458，会议密码：0609

报告题目一： Bootstrap Tests for High-Dimensional White-Noise

摘要： The testing of white-noise (WN) is an essential step in time series analysis. In a high dimensional set-up, most existing methods either are computationally infeasible, or suffer from highly distorted Type-I errors, or both. We propose an easy-to-implement bootstrap method for high-dimensional WN test and prove its consistency for a variety of test statistics. Its power properties as well as extensions to WN tests based on fitted residuals are also considered. Simulation results show that compared to the existing methods, the new approach possesses much better power, while maintaining a proper control over the Type-I error. They also provide proofs that even in cases where our method is expected to suffer from lack of theoretical justification, it continues to outperform its competitors. The proposed method is applied to the analysis of the daily stock returns of the top 50 companies by market capitalization listed on the NYSE, and we find strong evidence that the common market factor is the main cause of cross-correlation between stocks.

报告人简介： 孔婀芳, 电子科技大学数学科学学院教授、博士生导师。2006年获得新加坡国立大学统计专业博士毕业。曾任教于英国肯特大学。主要研究方向是半参数和非参数统计和渐近理论，计量经济模型和高维统计学习及推断等。研究成果曾发表于Annals of Statistics、JASA、Biometrika、Statistica Sinica、JBES、Econometric Theory 等统计和计量经济顶级刊物。

报告题目二： 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，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。