Graph-based change-point detection
TL;DR: In this paper, a nonparametric graph-based approach is proposed to detect change points in a data sequence, which can be applied to any data set as long as an informative similarity measure on the sample space can be defined.
read more
Abstract: We consider the testing and estimation of change-points—locations where the distribution abruptly changes—in a data sequence. A new approach, based on scan statistics utilizing graphs representing the similarity between observations, is proposed. The graph-based approach is nonparametric, and can be applied to any data set as long as an informative similarity measure on the sample space can be defined. Accurate analytic approximations to the significance of graph-based scan statistics for both the single change-point and the changed interval alternatives are provided. Simulations reveal that the new approach has better power than existing approaches when the dimension of the data is moderate to high. The new approach is illustrated on two applications: The determination of authorship of a classic novel, and the detection of change in a network over time.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A survey of methods for time series change point detection
TL;DR: This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series, and presents some grand challenges for the community to consider.
1.1K
•Posted Content
A kernel multiple change-point algorithm via model selection
TL;DR: In this paper, a penalty for choosing the number of change-points in the kernel-based method of Harchaoui and Capp{\'e} was proposed.
Graph-based change-point detection
TL;DR: In this paper, a nonparametric graph-based approach is proposed to detect change points in a data sequence, which can be applied to any data set as long as an informative similarity measure on the sample space can be defined.
169
Nonparametric maximum likelihood approach to multiple change-point problems
TL;DR: In this article, the authors proposed a nonparametric maximum likelihood approach to detect multiple change-points in the data sequence, which does not impose any parametric assumption on the underlying distributions.
158
Sequential change-point detection based on nearest neighbors
TL;DR: A new framework for the detection of change-points in online, sequential data analysis that utilizes nearest neighbor information and can be applied to sequences of multivariate observations or non-Euclidean data objects, such as network data is proposed.
100
References
Circular binary segmentation for the analysis of array-based DNA copy number data.
TL;DR: A modification ofbinary segmentation is developed, which is called circular binary segmentation, to translate noisy intensity measurements into regions of equal copy number in DNA sequence copy number.
Inferring friendship network structure by using mobile phone data
TL;DR: It is demonstrated that it is possible to accurately infer 95% of friendships based on the observational data alone, where friend dyads demonstrate distinctive temporal and spatial patterns in their physical proximity and calling patterns that allow the prediction of individual-level outcomes such as job satisfaction.
Empirical Analysis of an Evolving Social Network
TL;DR: This work analyzed a dynamic social network comprising 43,553 students, faculty, and staff at a large university, in which interactions between individuals are inferred from time-stamped e-mail headers recorded over one academic year and are matched with affiliations and attributes.
1.9K
Multivariate Generalizations of the Wald-Wolfowitz and Smirnov Two-Sample Tests
TL;DR: In this paper, generalizations of the Wald-Wolfowitz runs statistic and the Smirnov maximum deviation statistic for the two-sample problem are presented based on the minimal spanning tree of the pooled sample points.
An online kernel change detection algorithm
TL;DR: This work considers the case where no accurate nor tractable model can be found, using a model-free approach, called Kernel change detection (KCD), and builds a dissimilarity measure in feature space between two sets of descriptors, shown to be asymptotically equivalent to the Fisher ratio in the Gaussian case.
335