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Parametric and Nonparametric Sequential Change Detection in R: The cpm Package

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  • Ross, Gordon J.

Abstract

The change point model framework introduced in Hawkins, Qiu, and Kang (2003) and Hawkins and Zamba (2005a) provides an effective and computationally efficient method for detecting multiple mean or variance change points in sequences of Gaussian random variables, when no prior information is available regarding the parameters of the distribution in the various segments. It has since been extended in various ways by Hawkins and Deng (2010), Ross, Tasoulis, and Adams (2011), Ross and Adams (2012) to allow for fully nonparametric change detection in non-Gaussian sequences, when no knowledge is available regarding even the distributional form of the sequence. Another extension comes from Ross and Adams (2011) and Ross (2014) which allows change detection in streams of Bernoulli and Exponential random variables respectively, again when the values of the parameters are unknown. This paper describes the R package cpm, which provides a fast implementation of all the above change point models in both batch (Phase I) and sequential (Phase II) settings, where the sequences may contain either a single or multiple change points.

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  • Ross, Gordon J., 2015. "Parametric and Nonparametric Sequential Change Detection in R: The cpm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i03).
    • Handle: RePEc:jss:jstsof:v:066:i03
    DOI: http://hdl.handle.net/10.18637/jss.v066.i03
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    References listed on IDEAS

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    1. Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002. "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
    2. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    3. Hawkins, Douglas M., 2001. "Fitting multiple change-point models to data," Computational Statistics & Data Analysis, Elsevier, vol. 37(3), pages 323-341, September.
    4. Erdman, Chandra & Emerson, John W., 2007. "bcp: An R Package for Performing a Bayesian Analysis of Change Point Problems," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i03).
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    Cited by:

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    3. Peter M C Harrison & Roberta Bianco & Maria Chait & Marcus T Pearce, 2020. "PPM-Decay: A computational model of auditory prediction with memory decay," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-41, November.
    4. Andreas Anastasiou & Piotr Fryzlewicz, 2022. "Detecting multiple generalized change-points by isolating single ones," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(2), pages 141-174, February.
    5. Corbet, Shaen & Lucey, Brian & Peat, Maurice & Vigne, Samuel, 2018. "Bitcoin Futures—What use are they?," Economics Letters, Elsevier, vol. 172(C), pages 23-27.
    6. Arjun Prakash & Nick James & Max Menzies & Gilad Francis, 2020. "Structural clustering of volatility regimes for dynamic trading strategies," Papers 2004.09963, arXiv.org, revised Nov 2021.
    7. Peter Nystrup & Bo William Hansen & Henrik Madsen & Erik Lindström, 2016. "Detecting change points in VIX and S&P 500: A new approach to dynamic asset allocation," Journal of Asset Management, Palgrave Macmillan, vol. 17(5), pages 361-374, September.
    8. Lykou, R. & Tsaklidis, G. & Papadimitriou, E., 2020. "Change point analysis on the Corinth Gulf (Greece) seismicity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    9. Park, Beum-Jo, 2022. "The COVID-19 pandemic, volatility, and trading behavior in the bitcoin futures market," Research in International Business and Finance, Elsevier, vol. 59(C).
    10. James, Nick & Menzies, Max & Chan, Jennifer, 2021. "Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    11. Michele Scagliarini & Rosanna Gualdi & Giuseppe Ottaviano & Antonietta Rizzo, 2023. "Detection of anomalous radioxenon concentrations: A distribution‐free approach," Environmetrics, John Wiley & Sons, Ltd., vol. 34(7), November.
    12. Nick James, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Papers 2101.00576, arXiv.org, revised Feb 2021.
    13. Lindeløv, Jonas Kristoffer, 2020. "mcp: An R Package for Regression With Multiple Change Points," OSF Preprints fzqxv, Center for Open Science.
    14. Rui Qiang & Eric Ruggieri, 2023. "Autocorrelation and Parameter Estimation in a Bayesian Change Point Model," Mathematics, MDPI, vol. 11(5), pages 1-22, February.
    15. Magda Monteiro & Marco Costa, 2023. "Change Point Detection by State Space Modeling of Long-Term Air Temperature Series in Europe," Stats, MDPI, vol. 6(1), pages 1-18, January.
    16. Yanlin Shi, 2023. "Long memory and regime switching in the stochastic volatility modelling," Annals of Operations Research, Springer, vol. 320(2), pages 999-1020, January.
    17. James, Nick, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    18. Nora M. Villanueva & Marta Sestelo & Miguel M. Fonseca & Javier Roca-Pardiñas, 2023. "seq2R: An R Package to Detect Change Points in DNA Sequences," Mathematics, MDPI, vol. 11(10), pages 1-20, May.
    19. Baolong Ying & Qijing Yan & Zehua Chen & Jinchao Du, 2024. "A sequential feature selection approach to change point detection in mean-shift change point models," Statistical Papers, Springer, vol. 65(6), pages 3893-3915, August.
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    21. Nick James & Max Menzies & Jennifer Chan, 2020. "Semi-metric portfolio optimization: a new algorithm reducing simultaneous asset shocks," Papers 2001.09404, arXiv.org, revised Mar 2023.

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