.png){kind=icon}
Return
Graph-Based Change-Point Analysis
.png){kind=icon}
OA
AI
DOI：10.1146/annurev-statistics-122121-033817.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recent technological advances allow for the collection of massive data in the study of complex phenomena over time and/or space in various fields. Many of these data involve sequences of high-dimensional or non-Euclidean measurements, where change-point analysis is a crucial early step in understanding the data. Segmentation, or offline change-point analysis, divides data into homogeneous temporal or spatial segments, making subsequent analysis easier; its online counterpart detects changes in sequentially observed data, allowing for real-time anomaly detection. This article reviews a nonparametric change-point analysis framework that utilizes graphs representing the similarity between observations. This framework can be applied to data as long as a reasonable dissimilarity distance among the observations can be defined. Thus, this framework can be applied to a wide range of applications, from high-dimensional data to non-Euclidean data, such as imaging data or network data. In addition, analytic formulas can be derived to control the false discoveries, making them easy off-the-shelf data analysis tools.
Keywords:
nonparametrics
graph-based tests
scan statistic
tail probability
high-dimensional data
network data
non-Euclidean data
Journal
IF:
8.7
Papers:
211
Citations:
2.8K
Organization
Cited Papers
ASYMPTOTIC DISTRIBUTION-FREE CHANGE- POINT DETECTION FOR MULTIVARIATE AND NON-EUCLIDEAN DATA.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAIShareSaveShareSaveA new computational method for the detection of horizontal gene transfer eventsNUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSaveShareSaveShareSaveShareSave
View more