Dynamic networks exhibit temporal patterns that vary across different time scales, all of which can potentially affect processes that take place on the network. However, most data-driven approaches used to model timevarying networks attempt to capture only a single characteristic time scale in isolation — typically associated with the short-time memory of a Markov chain or with long-time abrupt...

June 4, 2010 Xiaofeng Shao and Xianyang Zhang University of Illinois at Urbana-Champaign Abstract: This article considers the CUSUM-based (cumulative sum) test for a change point in a time series. In the case of testing for a mean shift, the traditional KolmogorovSmirnov test statistic involves a consistent long run variance estimator, which is needed to make the limiting null distribution free...

Recently in functional magnetic resonance imaging (fMRI) studies there has been an increased interest in understanding the dynamic manner in which brain regions communicate with one another, as subjects perform a set of experimental tasks or as their psychological state changes. Dynamic Connectivity Regression (DCR) is a data-driven technique used for detecting temporal change points in functio...

Segmented line regression has been used in many applications, and the problem of estimating the number of change-points in segmented line regression has been discussed in Kim et al. (2000). This paper studies asymptotic properties of the number of change-points selected by the permutation procedure of Kim et al. (2000). This procedure is based on a sequential application of likelihood ratio typ...

We propose an on-line algorithm for exact filtering of multiple changepoint problems. This algorithm enables simulation from the true joint posterior distribution of the number and position of the changepoints for a class of changepoint models. The computational cost of this exact algorithm is quadratic in the number of observations. We further show how resampling ideas from particle filters ca...

We consider the problem of detecting change points (structural changes) in long sequences of data, whether in a sequential fashion or not, and without assuming prior knowledge of the number of these change points. We reformulate this problem as the Bayesian filtering and smoothing of a non standard state space model. Towards this goal, we build a hybrid algorithm that relies on particle filteri...

Predicting species distributions at scales of regions to continents is often necessary, as largescale phenomena influence the distributions of spatially structured populations. Land use and land cover are important large-scale drivers of species distributions, and landscapes are known to create species occurrence thresholds, where small changes in a landscape characteristic results in abrupt ch...

We consider the problem of detecting change points (structural changes) in long sequences of data, whether in a sequential fashion or not, and without assuming prior knowledge of the number of these change points. We reformulate this problem as the Bayesian filtering and smoothing of a non standard state-space model. Towards this goal, we build a hybrid algorithm that relies on particle filteri...

We consider the multiple change–point problem for multivariate time series, including strongly dependent processes, with an unknown number of change–points. We assume that the covariance structure of the series changes abruptly at some unknown common change–point times. The proposed adaptive method is able to detect changes in multivariate i.i.d., weakly and strongly dependent series. This adap...

The paper deals with the identification of a stationary autoregressive model for a time series and the contemporary detection of a change in its mean. We adopt the Bayesian approch with weak prior information about the parameters of the models under comparison and an exact form of the likelihood function. When necessary, we resort to fractional Bayes factor to choose between models, and to impo...