.png){kind=icon}
Return
Consistent and powerful graph-based change-point test for high-dimensional data
.png){kind=icon}
OA
AI
DOI：10.1073/pnas.1702654114.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Bayesian-type statistic
shortest Hamilton path
ratio cut
minimum spanning tree
cell division
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
P
IF:
9.1
Papers:
10.8W
Citations:
73.5W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveMultiple-change-point detection for high dimensional time series via sparsified binary segmentationOAAI
OAAIShareSave View more