主 题: Graph-based change-point test for high-dimensional data
内容简介: A change-point test for high-dimensional data is presented by using a Bayesian-type statistic based on the shortest Hamiltonian path, and the change-point is estimated by using ratio cut. A permutation procedure is applied to approximate the significance of Bayesian-type statistics. The change-point test is proven to be consistent, and an error probability in change-point estimation is provided. The test is powerful against alternatives with a shift in variance and is accurate in change-point estimation, as shown in simulation studies. Its applicability in tracking cell division is illustrated.
报告人: 吴月华 教授 博导
时 间: 2018-05-21 16:00
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 |
