Variable Screening for Ultrahigh Dimensional Heterogeneous Data via Conditional Quantile Correlations
主 题: Variable Screening for Ultrahigh Dimensional Heterogeneous Data via Conditional Quantile Correlations
报告人: Shucong Zhang (Shanghai University of Finance and Economics)
时 间: 2018-01-03 10:30-11:30
地 点: Room 1418, Sciences Building No. 1
Abstract: In this talk, we propose a new conditional quantile correlation and establish its connection with conditional quantile regression coefficient functions. We further introduce a conditional quantile screening method based on this metric for varying coefficient models with ultrahigh dimensional features. Under some technical conditions, the proposed approach is shown to enjoy desirable theoretical properties, including ranking consistency and sure screening properties. The extent of the new method’s dimensionality reduction is also qualified. To reduce the false selection rate, an iterative algorithm is proposed for improving the accuracy of variable screening. We conduct simulation studies to demonstrate that the proposed screening method can perform reasonably well, and we illustrate the proposed methodology through a real data analysis.
About the speaker: 张书聪是上海财经大学统计与管理学院金融统计与风险管理专业博士研究生，导师是周勇教授。研究方向为超高维数据变量筛选、高维数据的假设检验、生存分析、分位数回归等。