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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. Philip Preuss & Ruprecht Puchstein & Holger Dette, 2015. "Detection of Multiple Structural Breaks in Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 654-668, June.
    2. Marc Lavielle & Eric Moulines, 2000. "Least‐squares Estimation of an Unknown Number of Shifts in a Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(1), pages 33-59, January.
    3. Haeran Cho & Piotr Fryzlewicz, 2015. "Multiple-change-point detection for high dimensional time series via sparsified binary segmentation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 475-507, March.
    4. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    5. Michael B. Teitz & Polly Bart, 1968. "Heuristic Methods for Estimating the Generalized Vertex Median of a Weighted Graph," Operations Research, INFORMS, vol. 16(5), pages 955-961, October.
    6. Ting Fung Ma & Chun Yip Yau, 2016. "A pairwise likelihood-based approach for changepoint detection in multivariate time series models," Biometrika, Biometrika Trust, vol. 103(2), pages 409-421.
    7. Davis, Richard A. & Lee, Thomas C.M. & Rodriguez-Yam, Gabriel A., 2006. "Structural Break Estimation for Nonstationary Time Series Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 223-239, March.
    8. Paul Fearnhead & Guillem Rigaill, 2019. "Changepoint Detection in the Presence of Outliers," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 169-183, January.
    9. Claudia Kirch & Birte Muhsal & Hernando Ombao, 2015. "Detection of Changes in Multivariate Time Series With Application to EEG Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1197-1216, September.
    10. Cho, Haeran & Fryzlewicz, Piotr, 2015. "Multiple-change-point detection for high dimensional time series via sparsified binary segmentation," LSE Research Online Documents on Economics 57147, London School of Economics and Political Science, LSE Library.
    11. Hongyuan Cao & Wei Biao Wu, 2015. "Changepoint estimation: another look at multiple testing problems," Biometrika, Biometrika Trust, vol. 102(4), pages 974-980.
    12. Tengyao Wang & Richard J. Samworth, 2018. "High dimensional change point estimation via sparse projection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(1), pages 57-83, January.
    13. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    14. Nancy R. Zhang & David O. Siegmund, 2007. "A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data," Biometrics, The International Biometric Society, vol. 63(1), pages 22-32, March.
    15. Fryzlewicz, Piotr, 2014. "Wild binary segmentation for multiple change-point detection," LSE Research Online Documents on Economics 57146, London School of Economics and Political Science, LSE Library.
    16. David S. Matteson & Nicholas A. James, 2014. "A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 334-345, March.
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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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