IDEAS home Printed from https://ideas.repec.org/a/spr/sankha/v86y2024i2d10.1007_s13171-024-00355-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13171-024-00355-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13171-024-00355-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    2. Charles Lindsey & Simon Sheather, 2010. "Power transformation via multivariate Box–Cox," Stata Journal, StataCorp LLC, vol. 10(1), pages 69-81, March.
    3. 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.
    4. 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.
    5. Hansen M. H & Yu B., 2001. "Model Selection and the Principle of Minimum Description Length," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 746-774, June.
    6. 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.
    7. Velilla, Santiago, 1993. "A note on the multivariate Box--Cox transformation to normality," Statistics & Probability Letters, Elsevier, vol. 17(4), pages 259-263, July.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xuwen Zhu & Yana Melnykov, 2022. "On Finite Mixture Modeling of Change-point Processes," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 3-22, March.
    2. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    3. Yana Melnykov & Xuwen Zhu & Volodymyr Melnykov, 2021. "Transformation mixture modeling for skewed data groups with heavy tails and scatter," Computational Statistics, Springer, vol. 36(1), pages 61-78, March.
    4. Bhandary, Madhusudan, 1996. "Test for generalized variance in signal processing," Statistics & Probability Letters, Elsevier, vol. 27(2), pages 155-162, April.
    5. Melnykov, Volodymyr & Zhu, Xuwen, 2018. "On model-based clustering of skewed matrix data," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 181-194.
    6. Kundu, Debasis & Mitra, Amit, 2001. "Estimating the number of signals of the damped exponential models," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 245-256, April.
    7. Zhu, Li-Xing & Ohtaki, Megu & Li, Yingxing, 2007. "On hybrid methods of inverse regression-based algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2621-2635, February.
    8. Zhu, Li-Ping & Zhu, Li-Xing, 2007. "On kernel method for sliced average variance estimation," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 970-991, May.
    9. Bai, Zhidong & Silverstein, Jack W., 2022. "A tribute to P.R. Krishnaiah," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    10. Seongkyoon Jeong & Jae Young Choi, 2012. "The taxonomy of research collaboration in science and technology: evidence from mechanical research through probabilistic clustering analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 719-735, June.
    11. Poskitt, D.S. & Sengarapillai, Arivalzahan, 2013. "Description length and dimensionality reduction in functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 98-113.
    12. Wenbiao Zhao & Lixing Zhu & Falong Tan, 2024. "Multiple change point detection for high-dimensional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(3), pages 809-846, September.
    13. Yann Guédon, 2013. "Exploring the latent segmentation space for the assessment of multiple change-point models," Computational Statistics, Springer, vol. 28(6), pages 2641-2678, December.
    14. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    15. Debi P Bal & Badri N Rath, 2019. "Nonlinear causality between crude oil price and exchange rate: A comparative study of China and India - A Reassessment," Economics Bulletin, AccessEcon, vol. 39(1), pages 592-604.
    16. Njindan Iyke, Bernard, 2015. "Macro Determinants of the Real Exchange Rate in a Small Open Small Island Economy: Evidence from Mauritius via BMA," MPRA Paper 68968, University Library of Munich, Germany.
    17. Lee Jaeeun & Chen Jie, 2019. "A penalized regression approach for DNA copy number study using the sequencing data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(4), pages 1-14, August.
    18. Paul L. Bowen & Robert A. O'Farrell & Fiona H. Rohde, 2009. "An Empirical Investigation of End-User Query Development: The Effects of Improved Model Expressiveness vs. Complexity," Information Systems Research, INFORMS, vol. 20(4), pages 565-584, December.
    19. Chulwoo Han & Abderrahim Taamouti, 2017. "Partial Structural Break Identification," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(2), pages 145-164, April.
    20. Yingqiu Zhu & Qiong Deng & Danyang Huang & Bingyi Jing & Bo Zhang, 2021. "Clustering based on Kolmogorov–Smirnov statistic with application to bank card transaction data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 558-578, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sankha:v:86:y:2024:i:2:d:10.1007_s13171-024-00355-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.