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Yau, C.Y. and Zhao, Z. (2015) Inference for Multiple Change Points in Time Series via Likelihood Ratio Scan Statistics. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78, 895-916.
https://doi.org/10.1111/rssb.12139

has been cited by the following article:

• TITLE: Limit Theory of Model Order Change-Point Estimator for GARCH Models

  AUTHORS: Irene W. Irungu, Peter N. Mwita, Antony G. Waititu

  KEYWORDS: Autocorrelation Function, Change-Point, Convergence, GARCH, Manhattan Distance, Model Order, Point Process, Regular Variation, Weak Limit

  JOURNAL NAME: Journal of Mathematical Finance, Vol.8 No.2, May 28, 2018

  ABSTRACT: The limit theory of a change-point process which is based on the Manhattan distance of the sample autocorrelation function with applications to GARCH processes is examined. The general theory of the sample autocovariance and sample autocorrelation functions of a stationary GARCH process forms the basis of this study. Specifically the point processes theory is utilized to obtain their weak convergence limit at different lags. This is further extended to the change-point process. The limits are found to be generally random as a result of the infinite variance.