Change‐point detection in panel data

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Multiple‐change‐point detection for high dimensional time series via sparsified binary segmentation (2015, methodology): “Lavielle and Teyssière (2006) introduced a procedure based on penalised Gaussian log-likelihood as a cost function, where the estimator was computed via dynamic programming. The performance of the method was tested on bivariate examples. Vert and Bleakley (2010) proposed a method for approximating m”
Change-point detection in panel data via double CUSUM statistic (2016, methodology): “Some recent efforts in this direction include Jentsch and Politis (2015), who proposed the Linear Process Bootstrap for multivariate time series and established its asymptotic validity for the sample mean when n is allowed to increase with T .”
Uniform change point tests in high dimension (2015, background): “In [31], the stability of panel data is considered.”
Extensions of some classical methods in change point analysis (2014, methodology): “In this section we discuss the CUSUM-based testing method of Horváth and Hušková (2012).”