主题：High-dimensional Sliced Inverse Kendall’s tau Estimation for Heavy-tailed Data
主讲人：陈欣 南方科技大学
主持人：姜云卢 暨南大学
时间：2025年11月25日（周二）上午9:30-10:30
地点：暨南大学石牌校区经济学院大楼（中惠楼）323室
摘要
Sufficient dimension reduction is an important branch of statistical learning, which performs an optimal projection of covariates under supervised learning framework. Most of the existing sufficient dimension reduction algorithms require strict restrictions on the distribution of models, making them sensitive to heavy-tailed predictors and outliers. In this article, we propose a novel method called Sliced Inverse Kendall's tau Estimation (SIKE) designed for analyzing heavy-tailed, elliptically distributed and high-dimensional data that find wide application, especially in finance and economics. Compared with other existing methods, SIKE does not require computing median or conditional median, thus imposes milder conditions on the method and increases the precision of estimation. Furthermore, a remedial algorithm is introduced during discouraging situation, which enhances the general adaptability of our method. We investigate the theoretical properties of SIKE as the dimension p diverges with sample size n. Extensive simulation studies show that SIKE performs significantly well for various scenarios. Analyses of an asset pricing data and a housing price data also demonstrate the effectiveness of our proposed method.
主讲人简介
陈欣目前任职于南方科技大学统计与数据科学系副教授，研究员，博士生导师。1999年本科毕业于南开大学数学系，2003年在新加坡国立大学获得硕士学位。2010年博士毕业于美国明尼苏达大学双子城分校。曾在美国雪城大学，新加坡国立大学任教。主要研究领域是高维数据的降维和变量选择的方法, 其他的研究领域包括复杂数据分析，以及用统计方法研究气候变化。在统计学顶级刊物Annals of Statistics和 Biometrika发表过若干篇文章。目前担任JCR一区杂志Biometrics以及Statistics & Computing的副主编。
欢迎感兴趣的师生参加！
校对|姜云卢
责编| 彭毅
初审| 姜云卢
终审发布| 何凌云
（来源：暨南大学经济学院微信公众号）
