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Cho, H. and Fryzlewicz, P. (2015) Multiple-Change-Point Detection for High Dimensional Time Series via Sparsified Binary Segmentation. Journal of the Royal Statistical Society: Series B, 77, 475-507.
https://doi.org/10.1111/rssb.12079

has been cited by the following article:

• TITLE: Changepoint Detection with Outliers Based on RWPCA

  AUTHORS: Xin Zhang, Sanzhi Shi, Yuting Guo

  KEYWORDS: RWPCA-RFPOP, Double Robust, Outlier Detection, Biweight Loss

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.12 No.7, July 30, 2024

  ABSTRACT: Changepoint detection faces challenges when outlier data are present. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method, RWPCA-RFPOP method. Our method is double robust which is suitable for detecting mean changepoints in multivariate normal data with high correlations between variables that include outliers. Simulation results demonstrate that our method provides strong guarantees on both the number and location of changepoints in the presence of outliers. Finally, our method is well applied in an ACGH dataset.