We consider the testing and estimation of change-points, locations where distribution abruptly changes, in a sequence observations. Motivated by this problem, contribution we first investigate extremes Gaussian fields with trend, which then help us to give asymptotic p-value approximations likelihood ratio statistics from change-point models.

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...

In the asymptotic setting of the change-point estimation problem the limiting behavior of Bayes procedures for a general loss function is studied. It is demonstrated that the distribution of the diierence between the Bayes estimator and the parameter converges to the distribution of a (nondegenerate) random variable. The sequence of minimum Bayes risks is shown to converge to its supremum, and ...

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...