Feature Screening with Large Scale and High Dimensional Censored Data
报告题目：Feature Screening with Large Scale and High Dimensional Censored Data
报告专家：Professor Wenqing He（University of Western Ontario）
Data with a huge size present great challenges in modeling, inferences, and computation. In handling big data, much attention has been directed to settings with “large p small n”, and relatively less work has been done to address problems with p and n being both large, though data with such a feature have now become more accessible than before. To carry out valid statistical analysis, it is imperative to screen out noisy variables that have no predictive value for explaining the outcome variable. In this talk, we present a screening method for handling large sized survival data, where the sample size n is large and the dimension p of covariates is of nonpolynomial order of the sample size. We rigorously establish theoretical results for the proposed method and conduct numerical studies to assess its performance. Our research offers multiple extensions of existing work and enlarges the scope of high-dimensional data analysis. The proposed method capitalizes on the connections among useful regression settings and offers a computationally efficient screening procedure.
2002年从滑铁卢大学获得统计学哲学博士学位，2004 加入西安大略大学，现为西安大略大学统计与精算科学系教授、博士生导师。Wenqing He教授长期从事统计的理论和应用的研究工作，其研究领域涉及生存分析, 高维数据分析，统计学习，统计计算等。先后在国际统计学top期刊《The Journal of the Royal Statistical Society, Series B》，生物信息top期刊《Bioinformatics》, 以及一些著名期刊《Biometrics》，《Statistica Sinica》，《Technometrics》，《Journal of Multivariate Analysis》,《Statistics in Medicine》等国际权威刊物上发表论文七十余篇。现为Canadian Journal of Statistics，Statistics in Bioscience, Journal of Statistical Distributions and Applications 等杂志副主编。