Change point detection algorithms have numerous applications in areas of medical condition monitoring, fault industrial processes, human activity analysis, climate change detection, and speech recognition. We consider the problem on compositional multivariate data (each sample is a probability mass function), which practically important sub-class general data. While change-point well studied un...

We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which is an unrealistic assumption in typical applied problems. Instead we propose a new method for change-point detection based on Inductive Conformal M...

We study change–point estimation using a two–stage (generalizable to multi–stage) sampling design in a regression setting Y = f(X) + 2. For the talk, the underlying regression function is assumed to be parametrically specified, with a single unknown jump discontinuity of interest. The experimenter is given a budget of n points to make inference on the change–point. The two–stage procedure works...

Ecological and biological processes can change from one state to another once a threshold has been crossed in space or time. Threshold responses to incremental changes in underlying variables can characterize diverse processes from climate change to the desertification of arid lands from overgrazing. Simultaneously estimating the location of thresholds and associated ecological parameters can b...

Since the work of Page in the 1950s, the problem of detecting an abrupt change in the distribution of stochastic processes has received a great deal of attention. In particular, a deep connection has been established between Lorden’s minimax approach to change-point detection and the widely used CUSUM procedure, first for discrete-time processes, and subsequently for some of their continuous-ti...