Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where ...

I N STATISTICAL process control (SPC), one major concern is whether there has been a change of the distribution from the target in the process. To answer this question, we have to distinguish the variation due to real change of distribution (assignable causes) from that due to random error (chance causes). Many kinds of control charts serve this purpose. When there is no change and the process ...

The contribution is focused on change point detection in a one-dimensional stochastic process by sparse parameter estimation from an overparametrized model. A stochastic process with change in the mean is estimated using dictionary consisting of Heaviside functions. The basis pursuit algorithm is used to get sparse parameter estimates. The mentioned method of change point detection in a stochas...

We propose a change point detection algorithm for a sequence of graphs. Our algorithm focuses on the change of the structure of densely connected subgraphs (community structure) rather than the change of the link weights. In contrast to the traditional approaches, the algorithm can identify the structure change more sensitively. Experiments with a synthetic data and a real-world data of graphs ...

This work presents a novel approach to detect a change in the state of a signal. We propose that in a given state, the values of a signal vary in a subrange of a Gaussian distribution. We describe methods to monitor a signal in real time for change points based upon sub-Gaussian fitting. The proposed algorithm was implemented and tested on heartrate variability data.

MOTIVATION A non-parametric method, based on a wavelet data-dependent threshold technique for change-point analysis, is applied to predict location and topology of helices in transmembrane proteins. A new propensity scale generated from a transmembrane helix database is proposed. RESULTS We show that wavelet change-point performs well for smoothing hydropathy and transmembrane profiles genera...

We study the problem of detecting a change in the mean of one-dimensional Gaussian process data. This problem is investigated in the setting of increasing domain (customarily employed in time series analysis) and in the setting of fixed domain (typically arising in spatial data analysis). We propose a detection method based on the generalized likelihood ratio test (GLRT), and show that our meth...

The paper deals with the asymptotic distribution of the least squares estimator of a change point in a regression model where the regression function has two phases — the first linear and the second quadratic. In the case when the linear coefficient after change is non-zero the limit distribution of the change point estimator is normal whereas it is non-normal if the linear coefficient is zero.

The contribution deals with use of overcomplete models and sparse parameter estimation for change point detection in one–dimensional stochastic processes. These processes are estimated by ’Heaviside’ functions. The BASIS PURSUIT algorithm is used to get sparse parameter estimation. The mentioned method of change point detection in stochastic processes is compared with standard methods by simula...

Existing methods for bar code signal reconstruction is based on either the local approach or the regularization approach with total variation penalty. We formulate the problem explicitly in terms of change points of the 0-1 step function. The bar code is then reconstructed by solving the nonlinear least squares problem subject to linear inequality constraints, with starting values provided by t...