Journal ArticleDOI
Optimal segmentation of random processes
TLDR
One of the interests of the method is its ability to give the best solution, according to the resolution level required by the user, that is, to the prior distribution chosen.Abstract:
Segmentation of a nonstationary process consists in assuming piecewise stationarity and in detecting the instants of change. We consider the case where all the data is available at the same time and perform a global segmentation instead of a sequential procedure. We build a change process and define arbitrarily its prior distribution. This allows us to propose the MAP estimate as well as some minimum contrast estimate as a solution. One of the interests of the method is its ability to give the best solution, according to the resolution level required by the user, that is, to the prior distribution chosen. The method can address a wide class of parametric and nonparametric models. Simulations and applications to real data are proposed.read more
Citations
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Journal ArticleDOI
Selective review of offline change point detection methods
TL;DR: In this article, the authors present a selective survey of algorithms for the offline detection of multiple change points in multivariate time series, and a general yet structuring methodological strategy is adopted to organize this vast body of work.
Journal ArticleDOI
Structural Break Estimation for Nonstationary Time Series Models
TL;DR: This article considers the problem of modeling a class of nonstationary time series using piecewise autoregressive (AR) processes, and the minimum description length principle is applied to compare various segmented AR fits to the data.
Journal ArticleDOI
Detection and correction of artificial shifts in climate series
Henri Caussinus,Olivier Mestre +1 more
TL;DR: An example concerning temperature series from France confirms that a systematic comparison of the series together is valuable and allows us to correct the data even when no reliable series can be taken as a reference.
Journal ArticleDOI
Detection of multiple changes in a sequence of dependent variables
TL;DR: In this paper, the authors present convergence results for a minimum contrast estimator in a problem of change-points estimation, where the changes affect the marginal distribution of a sequence of random variables.
Journal ArticleDOI
Detection of Undocumented Changepoints Using Multiple Test Statistics and Composite Reference Series.
TL;DR: In this paper, an evaluation of three hypothesis test statistics that are commonly used in the detection of undocumented changepoints is described, and the importance of the form of the composite climate reference series is evaluated, particularly with regard to the impact of undocumented changes in the various component series that are used to calculate the composite.
References
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Journal ArticleDOI
On the statistical analysis of dirty pictures
TL;DR: In this paper, the authors proposed an iterative method for scene reconstruction based on a non-degenerate Markov Random Field (MRF) model, where the local characteristics of the original scene can be represented by a nondegenerate MRF and the reconstruction can be estimated according to standard criteria.
Journal ArticleDOI
Detection of abrupt changes: theory and application
TL;DR: A unified framework for the design and the performance analysis of the algorithms for solving change detection problems and links with the analytical redundancy approach to fault detection in linear systems are established.
BookDOI
Adaptive Algorithms and Stochastic Approximations
TL;DR: The juxtaposition of these two expressions in the title reflects the ambition of the authors to produce a reference work, both for engineers who use adaptive algorithms and for probabilists or statisticians who would like to study stochastic approximations in terms of problems arising from real applications.
Journal ArticleDOI
Inference about the change-point in a sequence of binomial variables
TL;DR: In this paper, the problem of making inference about the point in a sequence of zero-one variables at which the binomial parameter changes is discussed, and the asymptotic distribution of the maximum likelihood estimate of the change-point is derived in computable form using random walk results.
Journal ArticleDOI
Testing and Estimating Change-Points in Time Series
TL;DR: In this article, the authors present a few techniques which may be useful in the analysis of time series when a failure is suspected and investigate their asymptotic properties: one, of nonparametric type, is intended to detect a general failure in spectrum; the other, of likelihood ratio tests in parametric models which have a nonstandard behaviour in this situation.