The Multiple Change-Points Problem for the Spectral Distribution

By Marc Lavielle, C. Ludeña. 2000

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Detection of Multiple Structural Breaks in Multivariate Time Series (2013, methodology): “Lavielle and Ludena (2000) used penalized methods for identifying structural breaks and Lee et al. (2003) proposed a CUSUM-type statistic for detecting changes in specific parameters.”
Using penalized contrasts for the change-point problem (2005, background): “Then, the minimum penalized contrast (MPC) estimate of K is K̂MPC ¼ max 1pKpKMAX " 1f such that DK4Sg. ð28Þ K̂MPC is defined as the greatest value of K such that the second derivative of J is greater than a given threshold S.”
Multiple breaks detection in general causal time series using penalized quasi-likelihood (2012, methodology): “MR0245058 [17] Lavielle, M. and Ludena, C.”
Structural breaks in time series (2013, background): “The interested reader may refer Picard (1985), Adak (1998), Lavielle and Ludena (2000) and Ombao et al. (2001) for more information.”