报告题目：Local influence detection of conditional mean dependence

报告时间：2022/11/10 17:00-19:00 (GMT+08:00)中国标准时间-北京

腾讯会议：966-626-407

会议密码：654321

报告摘要：This article is focused on the problem to measure and test the condi- tional mean dependence of a response variable on a predictor variable. A local influence detection approach is developed combining with the martingale difference divergence (MDD) metric, and an efficient wild bootstrap implementation is given. The obtained new metric of the conditional mean dependence holds the merits of MDD, while it is more sensitive than the original one, and leads to a powerful test to nonlinear relationships. It is shown by simulations that the proposed test can achieve higher power for general conditional mean dependence relationships even in high dimensional settings. Theoretical asymptotic properties of the local influence test statistic are given, and a real data analysis is also presented for further illustration. The localization idea could be combined with other conditional mean dependence metrics.

报告人简介：

赖廷煜博士，广西北海人。本科、硕士均毕业于广西师范大学，所学专业分别为物理学和统计学，博士毕业于北京工业大学，统计学专业，研究方向为函数型数据分析。博士毕业后就职于广西师范大学数学与统计学院。在《Journal of Multivariate Analysis》《Computational Statistics & Data Analysis》等期刊上发表多篇文章。