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

Shi X,Wu Y,Rao CR

doi

10.1073/pnas.1702654114

subject

Has Abstract

pub_date

2017-04-11 00:00:00

pages

3873-3878

issue

15

eissn

0027-8424

issn

1091-6490

pii

1702654114

journal_volume

114

pub_type

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