Detection of multiple change-points in multivariate time series

By Marc Lavielle, Gilles Teyssière. 2006

192 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/2f4fb1e15f88619c847abd1b3c02fceac00def7e

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Citations in the corpus

A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data (2013, methodology): “As such, bisection (Vostrikova, 1981; Cho and Fryzlewicz, 2012) and model selection procedures (Lavielle and Teyssière, 2006; Arlot et al., 2012) are popular under these conditions.”
Detecting changes in the mean of functional observations (2009, background): “Different multivariate settings with further references are discussed in Lavielle and Teyssiére (2006), Zamba and Hawkins (2006) and Qu and Perron (2007), among others.”
Conditional Correlation Models of Autoregressive Conditional Heteroscedasticity With Nonstationary GARCH Equations (2014, methodology): “The problem of structural breaks in the conditional variance can be dealt with by assuming that the ARCH or GARCH model is piecewise stationary and detecting the breaks; see, for example, Berkes et al. (2004), or Lavielle and Teyssière (2006) for the multivariate case.”
Change-point detection in high-dimensional covariance structure (2016, background): “Most of them cover the case of fixed dimensionality [31] [29] [1] [41] [42].”
Multiple‐change‐point detection for high dimensional time series via sparsified binary segmentation (2015, methodology): “Lavielle and Teyssière (2006) introduced a procedure based on the penalized Gaussian log-likelihood as a cost function, where the estimator was computed via dynamic programming.”
Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation (2019, methodology): “The proposed method is able to estimate both the number of change points and their respective locations, consequently removing the need for previous knowledge or supplementary analysis, unlike the methods proposed in [1], [2], and [3].”
Bivariate frequency analysis of nonstationary low‐flow series based on the time‐varying copula (2015, methodology): “Using the method of detecting the change point in multivariate time series described by Lavielle and Teyssière (2006), it is found that”