The detection of change-points in a spatially or time ordered data sequence is an important problem in many fields such as genetics and finance. We derive the asymptotic distribution of a statistic recently suggested for detecting change-points. Simulation of its estimated limit distribution leads to a new and computationally efficient change-point detection algorithm, which can be used on very...

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 ...

This paper explores a Bayesian method for the detection of sudden changes in the generative parameters of a data series. The problem is phrased as a hidden Markov model, where change point locations correspond to unobserved states, which grow in number with the number of observations. Our interest lies in the marginal change point posterior density. Rather than optimize a likelihood function of...

Change-points in time series data are usually defined as the instants at which changes their properties occur. Detecting change-points is critical a number of applications diverse detecting credit card and insurance frauds, or intrusions into networks. Recently authors introduced an online kernel-based change-point detection method built upon direct estimation density ratio on consecutive inter...