主  題: Graph-based change-point test for high-dimensional data

內容簡介: 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.
報告人: 吳月華      教授    博導

時  間: 2018-05-21    16:00

地  點: 競慧東樓302

舉辦單位:統計與數學學院  統計科學與大數據研究院