title: Consistent and powerful graph-based change-point test for high-dimensional data.
abstract: A change-point detection is proposed 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 very 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.
authors.affiliation: Department of Mathematics and Statistics, Thompson Rivers University, Kamloops, BC, Canada V2C0C8; crr1@psu.edu xshi@tru.ca wuyh@mathstat.yorku.ca.
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Shi
authors.affiliation: Department of Mathematics and Statistics, York University, Toronto, ON, Canada M3J1P3; crr1@psu.edu xshi@tru.ca wuyh@mathstat.yorku.ca.
authors.forename: Yuehua
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Department of Biostatistics, University at Buffalo, The State University of New York, Buffalo, NY 14221-3000; crr1@psu.edu xshi@tru.ca wuyh@mathstat.yorku.ca.|C.R. Rao Advanced Institute of Mathematics, Statistics, and Computer Science, Hyderabad 500046, India.
authors.forename: Calyampudi Radhakrishna
authors.initials: CR
authors.lastname: Rao
keywords: Bayesian-type statistic; cell division; minimum spanning tree; ratio cut; shortest Hamilton path