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ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data

Author

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  • James, Nicholas A.
  • Matteson, David S.

Abstract

There are many different ways in which change point analysis can be performed, from purely parametric methods to those that are distribution free. The ecp package is designed to perform multiple change point analysis while making as few assumptions as possible. While many other change point methods are applicable only for univariate data, this R package is suitable for both univariate and multivariate observations. Hierarchical estimation can be based upon either a divisive or agglomerative algorithm. Divisive estimation sequentially identifies change points via a bisection algorithm. The agglomerative algorithm estimates change point locations by determining an optimal segmentation. Both approaches are able to detect any type of distributional change within the data. This provides an advantage over many existing change point algorithms which are only able to detect changes within the marginal distributions.

Suggested Citation

  • James, Nicholas A. & Matteson, David S., 2015. "ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i07).
    • Handle: RePEc:jss:jstsof:v:062:i07
    DOI: http://hdl.handle.net/10.18637/jss.v062.i07
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    References listed on IDEAS

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    1. Zeileis, Achim & Kleiber, Christian & Kramer, Walter & Hornik, Kurt, 2003. "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 109-123, October.
    2. Gandy, Axel, 2009. "Sequential Implementation of Monte Carlo Tests With Uniformly Bounded Resampling Risk," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1504-1511.
    3. Chang, Fang & Qiu, Weiliang & Zamar, Ruben H. & Lazarus, Ross & Wang, Xiaogang, 2010. "clues: An R Package for Nonparametric Clustering Based on Local Shrinking," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i04).
    4. 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.
    5. Makram Talih & Nicolas Hengartner, 2005. "Structural learning with time‐varying components: tracking the cross‐section of financial time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 321-341, June.
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    Cited by:

    1. Sara Major & Aleksandar Tomašević, 2025. "The face of populism: examining differences in facial emotional expressions of political leaders using machine learning," Journal of Computational Social Science, Springer, vol. 8(3), pages 1-22, August.
    2. Hang Xu & Philip L.H. Yu & Mayer Alvo, 2019. "Detecting change points in the stress‐strength reliability P(X," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(3), pages 837-857, May.
    3. repec:osf:osfxxx:fzqxv_v1 is not listed on IDEAS
    4. Bill Russell & Dooruj Rambaccussing, 2019. "Breaks and the statistical process of inflation: the case of estimating the ‘modern’ long-run Phillips curve," Empirical Economics, Springer, vol. 56(5), pages 1455-1475, May.
    5. Lindeløv, Jonas Kristoffer, 2020. "mcp: An R Package for Regression With Multiple Change Points," OSF Preprints fzqxv, Center for Open Science.
    6. Mengjia Yu & Xiaohui Chen, 2021. "Finite sample change point inference and identification for high‐dimensional mean vectors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 247-270, April.
    7. Zhou, Houlin & Zhu, Hanbing & Wang, Xuejun, 2025. "High-dimensional data analysis: Change point detection via bootstrap MOSUM," Journal of Multivariate Analysis, Elsevier, vol. 209(C).

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