Speaker: 周叶青（同济大学数学科学学院 特聘研究员）

Time/Date: 2024年3月19日（周二）12:00-13:00

Classroom: 同济大厦A楼505室

TENCENT meeting: 746721553

PW: 280431

Link: https://meeting.tencent.com/dm/8cJmPSoF8htD

Abstract:

Conditional independence is a fundamental concept in many scientific fields. In this article, we propose a projective approach to measuring and testing departure from conditional independence for dependent processes. Through projecting high-dimensional dependent processes on to low-dimensional subspaces, our proposed projective approach is insensitive to the dimensions of the processes. We show that, under the common β-mixing conditions, our proposed projective test statistic is n-consistent if these processes are conditionally independent and root-n-consistent otherwise. We suggest a bootstrap procedure to approximate the asymptotic null distribution of the test statistic. The consistency of this bootstrap procedure is also rigorously established. The finite-sample performance of our proposed projective test is demonstrated through simulations against various alternatives and an economic application to test for Granger causality.

Bio:

周叶青，同济大学数学科学学院特聘研究员。研究方向为高维数据降维、独立/条件独立检验。研究成果发表在Annals of Statistics、Journal of the American Statistical Association、Journal of Econometrics、Journal of Business & Economic Statistics等期刊上。