报告人：刘吉彩
时间：10月16日 14:30-15:30
地点：36-507
摘要：In this paper, we introduce a novel sample martingale difference correlation via data splitting to measure the departure of conditional mean independence between a response variable $Y$ and a vector predictor $\mathbf{X}$. The proposed correlation converges to zero and has an asymptotically symmetric sampling distribution around zero when $Y$ and $\mathbf{X}$ are conditionally mean independent. In contrast, it converges to a positive value when $Y$ and $\mathbf{X}$ are conditionally mean dependent. Leveraging these properties, we develop a new model-free feature screening method with false discovery rate (FDR) control for ultrahigh-dimensional data. We demonstrate that this screening method achieves FDR control and the sure screening property simultaneously. We also extend our approach to conditional quantile screening with FDR control. To further enhance the stability of the screening results, we implement multiple splitting techniques. We evaluate the finite sample performance of our proposed methods through simulations and real data analyses, and compare them with existing methods.
报告人简介：刘吉彩，上海立信会计金融学院统计与数学学院教授，硕士生导师。2013年12月毕业于华东师范大学获理学（统计学）博士学位，香港理工大学和香港城市大学访问学者。研究方向为生存数据统计分析、高维数据统计推断、统计机器学习等。主持完成2项国家自然科学基金项目，1项教育部人文社科项目。在Bernoulli，Journal of Computational and Graphical Statistics，Statistica Sinica，Science China Mathematics，Journal of Multivariate Analysis等国内外权威期刊上发表论文40余篇。现担任中国现场统计学会大数据统计分会常务理事，以及中国现场统计学会统计交叉科学研究分会、生存分析分会和青年统计学家协会理事。
