High-dimensional change-point detection under sparse alternatives

2019 ◽  
Vol 47 (4) ◽  
pp. 2051-2079 ◽  
Author(s):  
Farida Enikeeva ◽  
Zaid Harchaoui
Keyword(s):  
Change Point ◽  
Dimensional Change ◽  
Change Point Detection ◽  
High Dimensional ◽  
Point Detection

scholarly journals WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data

2021 ◽  
Author(s):  
Kamil Faber ◽  
Roberto Corizzo ◽  
Bartlomiej Sniezynski ◽  
Michael Baron ◽  
Nathalie Japkowicz
Keyword(s):  
Time Series ◽  
Change Point ◽  
Time Series Data ◽  
Change Point Detection ◽  
Series Data ◽  
High Dimensional ◽  
Point Detection

Unsupervised online change point detection in high-dimensional time series

2019 ◽  
Vol 62 (2) ◽  
pp. 719-750 ◽  
Author(s):  
Masoomeh Zameni ◽  
Amin Sadri ◽  
Zahra Ghafoori ◽  
Masud Moshtaghi ◽  
Flora D. Salim ◽  
...  
Keyword(s):  
Time Series ◽  
Change Point ◽  
Change Point Detection ◽  
High Dimensional ◽  
Point Detection

scholarly journals Consistent and powerful graph-based change-point test for high-dimensional data

2017 ◽  
Vol 114 (15) ◽  
pp. 3873-3878 ◽  
Author(s):  
Xiaoping Shi ◽  
Yuehua Wu ◽  
Calyampudi Radhakrishna Rao
Keyword(s):  
Change Point ◽  
Hamiltonian Path ◽  
Point Estimation ◽  
Change Point Detection ◽  
High Dimensional ◽  
Simulation Studies ◽  
Change Point Estimation ◽  
Change Point Test ◽  
Point Test ◽  
Point Detection

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.

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