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

內(nèi)容簡(jiǎn)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(bào)告人: 吳月華      教授    博導(dǎo)

時(shí)  間: 2018-05-21    16:00

地  點(diǎn): 競(jìng)慧東樓302