| 主题： |
Pairwise distance-based heteroscedasticity test for regressions |
| 类型： |
学术报告
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| 主办方： |
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| 报告人： |
蒋学军副研究员 |
| 日期： |
4月19日9:00-10:0 |
| 地点： |
知新楼B-1238 |
| 内容： |
报告题目： Pairwise distance-based heteroscedasticity test for regressions
报 告 人： 蒋学军（南方科技大学数学系助理教授，副研究员）
报告时间 ：4月19日（星期四），上午9:00-10:00
报告地点：知新楼B-1238 报告摘要：
In this paper, 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 derive a computationally feasible approximation for it. 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 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. 欢迎各位老师同学积极参加！
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