报告题目：Pairwise distance-based heteroscedasticity test for regressions

报告时间：2017年11月17日（周五） 9:00

报告地点：统计学院办公楼1层104会议室

主讲人：蒋学军，南方科技大学助理教授，广东省金融教指委委员

报告摘要：In this study, we propose a new test for heteroscedasticity of nonlinear regression models using a nonparametric statistic based on pairwise distances between points in a sample. The statistic can be formulated as a U statistic such that U-statistic theory can be applied. Although the limiting null distribution of the statistic is complicated, we derived a computationally feasible approximation for it.  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 greatly alleviates the impact of the curse of dimensionality. We include three simulation studies and two real data examples  to evaluate the performance of the test  and to demonstrate its applications.