IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v062i07.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v062i07/v62i07.pdf
    Download Restriction: no

    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v062i07/ecp_1.6.2.tar.gz
    Download Restriction: no

    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v062i07/v62i07-replication.zip
    Download Restriction: no

    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v062.i07?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
    ---><---

    References listed on IDEAS

    as
    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. 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).
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Lindeløv, Jonas Kristoffer, 2020. "mcp: An R Package for Regression With Multiple Change Points," OSF Preprints fzqxv, Center for Open Science.
    4. 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.

    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. DAVID E. ALLEN & MICHAEL McALEER & ROBERT J. POWELL & ABHAY K. SINGH, 2018. "Non-Parametric Multiple Change Point Analysis Of The Global Financial Crisis," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-23, June.
    2. Ariyarathne, Sakitha & Gangammanavar, Harsha & Sundararajan, Raanju R., 2022. "Change point detection-based simulation of nonstationary sub-hourly wind time series," Applied Energy, Elsevier, vol. 310(C).
    3. Kleiber, Christian, 2016. "Structural Change in (Economic) Time Series," Working papers 2016/06, Faculty of Business and Economics - University of Basel.
    4. Chang, Bi-Juan & Hung, Mao-Wei, 2021. "Corporate debt and cash decisions: A nonlinear panel data analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 15-37.
    5. Dong Ding & Axel Gandy & Georg Hahn, 2020. "A simple method for implementing Monte Carlo tests," Computational Statistics, Springer, vol. 35(3), pages 1373-1392, September.
    6. Yonglin Shen & Xiuguo Liu, 2015. "Phenological Changes of Corn and Soybeans over U.S. by Bayesian Change-Point Model," Sustainability, MDPI, vol. 7(6), pages 1-23, May.
    7. Baoni Li & Lihua Xiong & Quan Zhang & Shilei Chen & Han Yang & Shuhui Guo, 2022. "Effects of land use/cover change on atmospheric humidity in three urban agglomerations in the Yangtze River Economic Belt, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(1), pages 577-613, August.
    8. Guo, Zhichao & Feng, Yuanhua & Tan, Xiangyong, 2011. "Short- and long-term impact of remarkable economic events on the growth causes of China–Germany trade in agri-food products," Economic Modelling, Elsevier, vol. 28(6), pages 2359-2368.
    9. Grinis, Inna, 2017. "Trend growth durations & shifts," LSE Research Online Documents on Economics 85126, London School of Economics and Political Science, LSE Library.
    10. Fan, Ying & Xu, Jin-Hua, 2011. "What has driven oil prices since 2000? A structural change perspective," Energy Economics, Elsevier, vol. 33(6), pages 1082-1094.
    11. Zaldívar, José-Manuel & Strozzi, Fernanda & Dueri, Sibylle & Marinov, Dimitar & Zbilut, Joseph P., 2008. "Characterization of regime shifts in environmental time series with recurrence quantification analysis," Ecological Modelling, Elsevier, vol. 210(1), pages 58-70.
    12. Caporale, Guglielmo Maria & Gil-Alana, Luis A., 2008. "Modelling the US, UK and Japanese unemployment rates: Fractional integration and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4998-5013, July.
    13. Boako, Gideon & Alagidede, Paul, 2017. "Co-movement of Africa’s equity markets: Regional and global analysis in the frequency–time domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 359-380.
    14. Patrik Nosil & Zachariah Gompert & Daniel J. Funk, 2024. "Divergent dynamics of sexual and habitat isolation at the transition between stick insect populations and species," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    15. Mario Cimoli & Jose Antonio Ocampo & Gabriel Porcile & Nunzia Saporito, 2020. "Choosing sides in the trilemma: international financial cycles and structural change in developing economies," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 29(7), pages 740-761, October.
    16. De Santis, Paola & Drago, Carlo, 2014. "Asimmetria del rischio sistematico dei titoli immobiliari americani: nuove evidenze econometriche [Systematic Risk Asymmetry of the American Real Estate Securities: Some New Econometric Evidence]," MPRA Paper 59381, University Library of Munich, Germany.
    17. Karpf, Andreas & Mandel, Antoine & Battiston, Stefano, 2018. "Price and network dynamics in the European carbon market," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 103-122.
    18. Michael Messer, 2022. "Bivariate change point detection: Joint detection of changes in expectation and variance," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 886-916, June.
    19. Casson, Catherine & Fry, J. M., 2011. "Revolutionary change and structural breaks: A time series analysis of wages and commodity prices in Britain 1264-1913," MPRA Paper 27866, University Library of Munich, Germany.
    20. Noriah Al-Kandari & Emad-Eldin Aly, 2014. "An ANOVA-type test for multiple change points," Statistical Papers, Springer, vol. 55(4), pages 1159-1178, November.

    More about this item

    Statistics

    Access and download statistics

    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:jss:jstsof:v:062:i07. 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: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    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.