We introduce a kernel-based method for change-point analysis within a sequence of temporal observations. Change-point analysis of an unlabelled sample of observations consists in, first, testing whether a change in the distribution occurs within the sample, and second, if a change occurs, estimating the change-point instant after which the distribution of the observations switches from one dist...

Change-point methodologies applied to statistical process control are predicated on the possibility that a special cause induces a shift from an in-control statistical model to an out-ofcontrol statistical model, and so are particularly attractive for persistent special causes. Along with indications of a loss of control, they provide estimates of when the shift occurred, and (if needed) of the...

In the present paper, we develop a new efficient approach to detection of abrupt changes in a sequence of recognized digital images, which can be applied to a wide variety of practical problems of digital image processing. The principal idea of this approach consists in transforming an original sample of the observed data to a set of onedimensional statistics and measuring a distance from homog...

We tackle the change-point problem with data belonging to a general set. We propose a penalty for choosing the number of change-points in the kernel algorithm of Harchaoui and Cappé (2007). This penalty generalizes the one proposed for one dimensional signals by Lebarbier (2005). We prove it satisfies a non-asymptotic oracle inequality by showing new concentration results in Hilbert spaces. Exp...

No matter whether it is acoustic waves, quantum matter waves or optical waves of a laser—all kinds of waves can be in different states of oscillation, corresponding to different frequencies. Calculating these frequencies is part of the tools of the trade in theoretical physics. Recently, however, a special class of systems has caught the attention of the scientific community, forcing physicists...

Abstract: We propose new tests to detect a change in the mean of a time series. Like many existing tests, the new ones are based on the CUSUM process. Existing CUSUM tests require an estimator of a scale parameter to make them asymptotically distribution free under the no change null hypothesis. Even if the observations are independent, the estimation of the scale parameter is not simple since ...

Assuming a first-order auto-regressive model for the auto-correlation structure between observations, in this paper, a transformation method is first employed to eliminate the effect of auto-correlation. Then, a maximum likelihood estimator (MLE) of a step change in the parameters of the transformed model is derived and three separate EWMA control charts are used to monitor the parameters of th...

A Bayesian analysis is used to detect a change-point in a sequence of independent random variables from exponential distributions. In This paper, we try to estimate change point which occurs in any sequence of independent exponential observations. The Bayes estimators are derived for change point, the rate of exponential distribution before shift and the rate of exponential distribution after s...

Abstract. We present a Bayesian analysis for recurrent events data using a nonhomogeneous mixed Poisson point process with a dynamic subject-specific frailty function and a dynamic baseline intensity func- tion. The dynamic subject-specific frailty employs a dynamic piecewise constant function with a known pre-specified grid and the baseline in- tensity uses an unknown grid for the piecewise ...

Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step < /div> change, a linear trend and a known multip...