主 题: Efficient Monte Carlo evaluation of resampling-based hypothesis tests
内容简介: Monte Carlo evaluation of resampling-based tests is often conducted in statistical analysis. However, this procedure is generally computationally intensive. The pooling resampling-based method has been developed to reduce the computational burden but the validity of the method has not been studied before. In this talk, we first investigate the asymptotic properties of the pooling resampling-based method, and then propose a novel Monte Carlo evaluation procedure namely the n-times pooling resampling-based method. Theorems as well as simulations show that the proposed method can give smaller or comparable root mean squared errors and bias with much less computing time, thus can be strongly recommended especially for evaluating highly computationally intensive hypothesis testing procedures.
报告人: 冯荣锦 教授 博导
时 间: 2018-04-04 15:00
地 点: 竞慧东楼302
举办单位: 统计与数学学院 统计科学与大数据研究院
