报告题目：Directional Outlyingness for Multivariate Functional Data

报告时间：11月18日下午2：30—4：00

报告地点: 数学楼2-3会议室

报告人：代文林博士，中国人民大学统计与大数据研究院

报告摘要：

The direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, classical depth is generalized to directional outlyingness for functional data. Theoretical properties of functional directional outlyingness are investigated and the total outlyingness can be naturally decomposed into two parts: magnitude outlyingness and shape outlyingness which represent the centrality of a curve for magnitude and shape, respectively. This decomposition serves as a visualization tool for the centrality of curves. Furthermore, an outlier detection procedure is proposed based on functional directional outlyingness.

报告人简介：

代文林博士目前是中国人民大学统计与大数据研究院的助理教授、博士生导师。2014年毕业于香港浸会大学，获得博士学位。2015年赴沙特阿拉伯阿卜杜拉国王科技大学进行博士后研究。2018年9月加入中国人民大学统计与大数据研究院。主要研究领域包括非参数统计，函数型数据分析，空间统计学与数据可视化。目前已在Journal of Machine Learning Research，Statistical Science，Journal of Computational and Graphical Statistics等国际权威期刊上发表学术论文多篇。