报告题目：Variable selection for functional linear models with strong heredity constraint
报 告 人：冯三营 教授
工作单位：郑州大学数学与统计学院
报告时间：2024年5月31日8:30-10:30
报告地点：数信学院403
报告摘要：
In this paper, we consider the variable selection problem in functional linear regression with interactions. Our goal is to identify relevant main effects and corresponding interactions associated with the response variable. Heredity is a natural assumption in many statistical models involving two-way or higher-order interactions. Inspired by this, we propose an adaptive group Lasso method for the multiple functional linear model that adaptively selects important single functional predictors and pairwise interactions while obeying the strong heredity constraint. The proposed method is based on the functional principal components analysis with two adaptive group penalties, one for main effects and one for interaction effects. With appropriate selection of the tuning parameters, the rates of convergence of the proposed estimators and the consistency of the variable selection procedure are established. Simulation studies demonstrate the performance of the proposed procedure and a real example is analyzed to illustrate its practical usage.
报告人简介：
冯三营，统计学博士，郑州大学数学与统计学院教授，硕士生导师。主要研究方向：高维统计、非参数统计和复杂数据分析、变量选择、统计学习、面板数据分析等。主持完成和在研国家与省部级科研项目20余项。截至目前，在《Statistica Sinica》、《Journal of Multivariate Analysis》、《Computational Statistics and Data Analysis》等统计学专业期刊上发表和录用论文60余篇，其中SCI 收录论文40余篇，出版学术专著《现代测量误差模型》1 部。
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