报告题目：Nonparametric Screening for Additive Quantile Regression in Ultra-high Dimension
时 间：2024年5月31日（星期五） 15：30
地 点：理工北楼601
主 办：数学与统计学院、分析数学及应用教育部重点实验室、福建省分析数学及应用重点实验室、统计学与人工智能福建省高校重点实验室、福建省应用数学中心（福建师范大学）
参加对象：感兴趣的老师和学生
报告摘要：In practical applications, one often does not know the true structure of the underlying conditional quantile function, especially in the ultra-high dimensional setting. To deal with ultra-high dimensionality, quantile-adaptive marginal nonparametric screening methods have been recently developed. However, these approaches may miss important covariates that are marginally independent of the response, or may select unimportant covariates due to their high correlations with important covariates. To mitigate such shortcomings, we develop a conditional nonparametric quantile screening procedure (complemented by subsequent selection) for nonparametric additive quantile regression models. Under some mild conditions, we show that the proposed screening method can identify all relevant covariates in a small number of steps with probability approaching one. The subsequent narrowed best subset (via a modified Bayesian information criterion) also contains all the relevant covariates with overwhelming probability. The advantages of our proposed procedure are demonstrated through simulation studies and a real data example.
报告人简介：李道纪博士现为美国加州州立大学 Fullerton 校区(CSUF)商业与经济学院数据科学与统计学系副教授。早年于英国曼彻斯特大学获得统计学博士学位，在美国南加州大学数据科学与运筹系从事博士后研究，并曾任教于中佛罗里达大学统计与数据科学系。研究兴趣包括：高维统计，特征筛选深度学习，因果推断，纵向数据分析等；研究成果主要发表在统计学国际知名期刊The Annals of Statistics, Journal of Business & Economic Statistics, Journal of Business Research，Journal of Multivariate Analysis, Computational Statistics & Data Analysis等。