统计与管理学院2018年学术报告第49期

【主 题】 Category-Adaptive Variable Screening for Ultra-high Dimensional Categorical Data

【报告人】 林媛媛, 助教授

           香港中文大学统计学系

【时 间】 2018年12月19日（星期三）15:00-16:00

【地 点】 上海财经大学统计与管理学院大楼1208会议室

【摘 要】The populations of interest in modern studies are very often heterogeneous. The population heterogeneity, the qualitative nature of the outcome variable and the high dimensionality of the predictors pose significant challenge in statistical analysis. In this article, we introduce a category-adaptive screening procedure to detect category-specific important covariates for high-dimensional heterogeneous data. The proposal is a model-free approach without any specification of a regression model and an adaptive procedure in the sense that the set of active variables is allowed to vary across different categories, thus making it more flexible to accommodate heterogeneity. For response-selective sampling data, another main discovery of this paper is that the proposed method works directly without any modification. Under mild regularity conditions, the proposed procedure is shown to possess the sure screening and ranking consistency properties. Simulation studies contain supportive evidence that the proposed method performs well under various settings and it is effective to extract category-specific information. Applications are illustrated with two real data sets

【嘉宾简介】香港中文大学统计系助理教授，研究方向为生存数据及复杂数据分析、高维数据分析、个性化医疗、机器学习等，已经在包括JASA、Biometrika等国际顶尖期刊发表论文近20篇，主持多项香港研究基金项目。