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Most recent changepoint detection in censored panel data

Author

Listed:
  • Hajra Siddiqa

    (Quaid-i-Azam University)

  • Sajid Ali

    (Quaid-i-Azam University)

  • Ismail Shah

    (Quaid-i-Azam University)

Abstract

This study aims to detect the most recent changepoint in censored panel data by ignoring dependence within and between segments as well as taking into account the serial autocorrelation. A comparison of different methods to detect the most recent changepoint for censored data is presented. Different censoring rates such as 20%, 50%, and 90% in the case of right and left censoring while (10%, 10%), (25%, 25%) and (40%, 50%) for interval censoring are considered. Further, we use most recent changepoint (MRC), double cumulative sum binary segmentation, non parametric changepoint detection (ECP), multiple changepoints in multivariate time series, analyzing each series in the panel independently, and analyzing aggregated data (AGG) methods. It is observed that different censoring rates have a significant effect on the detection of changepoints in high dimensional data. It is also noticed that the MRC method outperforms the competing methods considered in this study. In addition to investigating the impact of penalties, the performance of MRC and AGG methods is also compared using water quality data of the Niagara River. Also, a data set related to survival time of stroke patients is also a part of this study. An R package “cpcens” is available in comprehensive R archive network to replicate the results of this article.

Suggested Citation

  • Hajra Siddiqa & Sajid Ali & Ismail Shah, 2021. "Most recent changepoint detection in censored panel data," Computational Statistics, Springer, vol. 36(1), pages 515-540, March.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:1:d:10.1007_s00180-020-01028-5
    DOI: 10.1007/s00180-020-01028-5
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    1. Tong Si & Yunge Wang & Lingling Zhang & Evan Richmond & Tae-Hyuk Ahn & Haijun Gong, 2024. "Multivariate Time Series Change-Point Detection with a Novel Pearson-like Scaled Bregman Divergence," Stats, MDPI, vol. 7(2), pages 1-19, May.

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