题　目：Cauchy-Weighted Measure for Nonlinear Conditional Dependencies within the High-Dimensional Factor Model Framework

嘉　宾：Ruoqing Jiang, Ph.D Candidate, Columbia University

主持人：吴瑞佳 助理教授 上海交通大学安泰经济与管理学院

时　间：2025年1月3日（周五）14:00-15:30

地　点：上海交通大学 徐汇校区 安泰浩然楼306室

内容简介: Conditional independence testing is a fundamental and challenging problem with many important applications. This paper proposes a new approach to test conditional independence within the framework of high-dimensional factor models. A Cauchy-weighted measure is introduced to quantify the dependence between the idiosyncratic components and develop a corresponding conditional independence test. This measure, which ranges from 0 to 1, equals 0 if and only if conditional independence is true. It is robust to extreme values and computationally efficient. The proposed test is asymptotically distribution-free under the null hypothesis and capable of detecting nonlinear dependencies under the alternative hypothesis in high-dimensional scenarios. Furthermore, this paper demonstrates that the approach of first directly estimating the factors using pooled data, followed by performing the test, is invalid when factors are unobserved. Instead, this paper proposes to estimate the factors within the factor-augmented regression model framework and shows that the corresponding test remains valid. Extensive simulation studies and real data analysis are conducted to validate the effectiveness of this method, demonstrating its superior performance in high-dimensional scenarios.

演讲人简介： Ruoqing Jiang is a PhD candidate at Columbia University. He holds a Bachelor's degree in Economics from Tsinghua University and a Master's degree in Statistics from Stanford University. His research focuses on statistical machine learning, econometrics, and their applications.

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