A Projective Approach to Conditional Independence Test for Dependent Processes
Mon, Mar 18, 2024
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。