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On Robust Change Point Detection and Estimation in Multisubject Studies

Author

Listed:
  • Yana Melnykov

    (The University of Alabama)

  • Marcus Perry

    (The University of Alabama)

Abstract

A variety of change point estimation and detection algorithms have been developed for random variables observed over time. The acquisition of data in current practice often results in multiple subjects studied. The traditional treatment of such observations involves the assumption of their independence. In practice, however, this assumption is often inadequate or unrealistic. We propose an effective and modern computerized approach to estimating and detecting change points in linear model time series processes in the situation when the assumption of independent observations is not feasible. The developed methodology relies on the multivariate transformation and matrix normal distribution. The latter is used for separating the sources of variability. The application of the back-transform of the exponential transformation leads to a flexible distribution that effectively accounts for deviations from normality. The developed procedure has been successfully tested in various settings and applied to a crime rate data set.

Suggested Citation

  • Yana Melnykov & Marcus Perry, 2024. "On Robust Change Point Detection and Estimation in Multisubject Studies," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(2), pages 827-879, August.
    • Handle: RePEc:spr:sankha:v:86:y:2024:i:2:d:10.1007_s13171-024-00355-9
    DOI: 10.1007/s13171-024-00355-9
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    References listed on IDEAS

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    1. Nancy R. Zhang & David O. Siegmund & Hanlee Ji & Jun Z. Li, 2010. "Detecting simultaneous changepoints in multiple sequences," Biometrika, Biometrika Trust, vol. 97(3), pages 631-645.
    2. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    3. Zhao, L. C. & Krishnaiah, P. R. & Bai, Z. D., 1986. "On detection of the number of signals in presence of white noise," Journal of Multivariate Analysis, Elsevier, vol. 20(1), pages 1-25, October.
    4. Sandipan Roy & Yves Atchadé & George Michailidis, 2017. "Change point estimation in high dimensional Markov random-field models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1187-1206, September.
    5. 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.
    6. Charles Lindsey & Simon Sheather, 2010. "Power transformation via multivariate Box–Cox," Stata Journal, StataCorp LP, vol. 10(1), pages 69-81, March.
    7. Zhao, L. C. & Krishnaiah, P. R. & Bai, Z. D., 1986. "On detection of the number of signals when the noise covariance matrix is arbitrary," Journal of Multivariate Analysis, Elsevier, vol. 20(1), pages 26-49, October.
    8. Velilla, Santiago, 1993. "A note on the multivariate Box--Cox transformation to normality," Statistics & Probability Letters, Elsevier, vol. 17(4), pages 259-263, July.
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