报告人:蒋学军 副教授(南方科技大学)
报告题目:Pairwise distance-based heteroscedasticity test for regressions
报告人简介:蒋学军,南方科技大学数学系长聘副教授,博士生导师已在统计学国际主流期刊和相关金融、经济等交叉学科期刊上发表SCI&SSCI论文40余篇。
报告摘要:In this study, we propose nonparametric testing for heteroscedasticity in nonlinear regression models based on pairwise distances between points in a sample. The test statistic can be formulated such that U-statistic theory can be applied to it. Although the limiting null distribution of the statistic is complicated, we can derive a computationally feasible bootstrap approximation for such a distribution; the validity of the introduced bootstrap algorithm is proven. The test can detect any local alternatives that are different from the null at a nearly optimal rate in hypothesis testing. The convergence rate of this test statistic does not depend on the dimension of the covariates, which significantly alleviates the impact of dimensionality. We provide three simulation studies and a real-data example to evaluate the performance of the test and demonstrate its applications.
报告时间:2019年9月14日(星期六)下午14:00 - 15:30.
报告地点:科技楼南楼702