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【主 题】 Dynamic Tilted Current Correlation for High Dimensional Variable Screening

【报告人】 Wenqing He, 教授

University of Western Ontario

【时 间】 2019年1218   16:00-17:00

【地 点】 上海财经大学统计与管理学院大楼1208会议室

 High dimensionality brings distorted result and computational burden to statistical analysis of this type of data. In the ultra-high dimensional setting, the theory of Sure Independence Screening was introduced to significantly reduce the dimensionality of variables to a moderate scale below the sample size and to preserve the true model with probability tending to 1.  The outstanding performance of SIS stimulates the researchers to investigate more and better methods on high dimensional variable screening. The performance of SIS depends on the marginal correlation which is unreliable when the dimension is high. In reality, the importance of the variables cannot be easily ranked by their marginal correlations when there are high correlations among predictor variables. Due to the dimensionality, important predictors may have small marginal correlations with the response, while unimportant predictors may be highly correlated with the response variable due to the associated or spurious correlation with the important predictors. To remove those unimportant predictors and keep real important predictors, we propose a new estimator for the correlation between the response and variables in high dimensional settings, and a new screening technique termed dynamic tilted current correlation screening (DTCCS) is employed to do the variable screening. The new method reduces high spurious correlation among predictor variables in a data-driven fashion. We show that DTCCS is able to discover all relevant predictor variables within a finite number of steps when the dimensional of the true model is finite. DTCCS's sure screening property, consistency property and computational complexity are justified theoretically and numerically.

嘉宾简介He Wenqing,加拿大西安大略大学的教授,博士生导师。主要从事多变量寿命数据分析、基因表达数据分析、纵向数据分析、缺失数据分析、数据挖掘和生物统计等领域的研究。目前已经在Journal of the Royal Statistical Society, Series BStatistics in MedicinesStatistica Sinica等国际期刊上发表论文四十余篇。