This paper investigates a change-point estimation problem in the context of high-dimensional Markov random field models. Change-points represent a key feature in many dynamically evolving network structures. The change-point estimate is obtained by maximizing a profile penalized pseudo-likelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logari...

The input-output delay of a process is an important parameter for control design and closed-loop identification. This paper proposes new methods for estimating the delay of a transfer function model in closed-loop based on statistical change-point detection methods. A Bayesian change-point approach, a sequential probability ratio test (SPRT), and a cumulative sum (CUSUM) approach are proposed a...

We consider high dimensional nonhomogeneous linear regression models with p n 9 0 or p >> n, where p is the number of features and n is the number of observations. In the model considered, the underlying true regression coefficients undergo multiple changes. Our goal is to estimate the number and locations of these change-points and estimate sparse coefficients in each of the intervals between ...

The contribution is focused on multiple change point detection in a onedimensional stochastic process by sparse parameter estimation from an overparametrized model. Stochastic process with changes in the mean is estimated using dictionary consisting of Heaviside functions. The basis pursuit algorithm is used to get sparse parameter estimates. Some properties of mentioned method are studied by s...

There are many different ways in which change point analysis can be performed, from purely parametric methods to those that are distribution free. The ecp package is designed to perform multiple change point analysis while making as few assumptions as possible. While many other change point methods are applicable only for univariate data, this R package is suitable for both univariate and multi...

Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed in the community of statistics and data mining. In this paper, we present a novel non-parametric approach to detecting the change of probability distributions of sequence data. Our key idea is to estimat...

Effects of many medical procedures appear after a time lag, when a significant change occurs in subjects’ failure rate. This paper focuses on the detection and estimation of such changes which is important for the evaluation and comparison of treatments and prediction of their effects. Unlike the classical change-point model, measurements may still be identically distributed, and the change poi...