报告题目 (Title)：基于Spearman秩相关矩阵的高维因子建模中因子数量的稳健估计
报告人 (Speaker)：李曾 副教授（南方科技大学）
报告时间 (Time)：2024年09月26日 (周四) 11:00-12：30
报告地点 (Place)：腾讯会议（会议号：824-463-386 无密码）
邀请人(Inviter)：张阳春
报告摘要：Determining the number of factors in high-dimensional factor modeling is essential but challenging, especially when the data are heavy-tailed. In this paper, we introduce a new estimator based on the spectral properties of Spearman’s rank correlation matrix under the high-dimensional setting, where both dimension and sample size tend to infinity proportionally. Our estimator is applicable for scenarios where either the common factors or idiosyncratic errors follow heavy-tailed distributions. We prove that the proposed estimator is consistent under mild conditions. Numerical experiments also demonstrate the superiority of our estimator compared to existing methods, especially for the heavy-tailed case.