IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v105y2017icp268-279.html
   My bibliography  Save this article

Sequential rank CUSUM charts for angular data

Author

Listed:
  • Lombard, F.
  • Hawkins, Douglas M.
  • Potgieter, Cornelis J.

Abstract

A cumulative sum (CUSUM) control chart has desirable properties for checking whether a distribution has changed from an in-control to an out-of-control setting. Distribution-free CUSUMs based on sequential ranks to detect changes in the mean direction and dispersion of angular data are developed and some of their properties are illustrated by theoretical calculations and Monte Carlo simulation. Three applications to sequentially observed angular data from health science, industrial quality control and astrophysics are discussed.

Suggested Citation

  • Lombard, F. & Hawkins, Douglas M. & Potgieter, Cornelis J., 2017. "Sequential rank CUSUM charts for angular data," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 268-279.
    • Handle: RePEc:eee:csdana:v:105:y:2017:i:c:p:268-279
    DOI: 10.1016/j.csda.2016.08.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947316301827
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2016.08.001?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. David McDonald, 1990. "A cusum procedure based on sequential ranks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 37(5), pages 627-646, October.
    2. Berens, Philipp, 2009. "CircStat: A MATLAB Toolbox for Circular Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i10).
    3. F. Lombard & R. K. Maxwell, 2012. "A cusum procedure to detect deviations from uniformity in angular data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 1871-1880, April.
    4. Taylor, Charles C., 2008. "Automatic bandwidth selection for circular density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3493-3500, March.
    5. Douglas M. Hawkins & F. Lombard, 2015. "Segmentation of circular data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(1), pages 88-97, January.
    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. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.

    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. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.
    2. Paula Saavedra-Nieves & Rosa M. Crujeiras, 2022. "Nonparametric estimation of directional highest density regions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 761-796, September.
    3. Thomas Schreiner & Marit Petzka & Tobias Staudigl & Bernhard P. Staresina, 2023. "Respiration modulates sleep oscillations and memory reactivation in humans," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    4. Thomas Schreiner & Elisabeth Kaufmann & Soheyl Noachtar & Jan-Hinnerk Mehrkens & Tobias Staudigl, 2022. "The human thalamus orchestrates neocortical oscillations during NREM sleep," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    5. Alireza Saeedi & Kun Wang & Ghazaleh Nikpourian & Andreas Bartels & Nikos K. Logothetis & Nelson K. Totah & Masataka Watanabe, 2024. "Brightness illusions drive a neuronal response in the primary visual cortex under top-down modulation," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    6. César Henrique Mattos Pires & Felipe M. Pimenta & Carla A. D'Aquino & Osvaldo R. Saavedra & Xuerui Mao & Arcilan T. Assireu, 2020. "Coastal Wind Power in Southern Santa Catarina, Brazil," Energies, MDPI, vol. 13(19), pages 1-23, October.
    7. Matthijs J. Warrens & Bunga C. Pratiwi, 2016. "Kappa Coefficients for Circular Classifications," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 507-522, October.
    8. Masataka Sawayama & Shin'ya Nishida, 2018. "Material and shape perception based on two types of intensity gradient information," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-40, April.
    9. Aguiar-Conraria, Luis & Martins, Manuel M.F. & Soares, Maria Joana, 2018. "Estimating the Taylor rule in the time-frequency domain," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 122-137.
    10. Daniel S. Kluger & Carina Forster & Omid Abbasi & Nikos Chalas & Arno Villringer & Joachim Gross, 2023. "Modulatory dynamics of periodic and aperiodic activity in respiration-brain coupling," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    11. Manuela Costa & Diego Lozano-Soldevilla & Antonio Gil-Nagel & Rafael Toledano & Carina R. Oehrn & Lukas Kunz & Mar Yebra & Costantino Mendez-Bertolo & Lennart Stieglitz & Johannes Sarnthein & Nikolai , 2022. "Aversive memory formation in humans involves an amygdala-hippocampus phase code," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    12. Toshinori Namba & Shuji Ishihara, 2020. "Cytoskeleton polarity is essential in determining orientational order in basal bodies of multi-ciliated cells," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-18, February.
    13. Pham Ngoc, Thanh Mai, 2019. "Adaptive optimal kernel density estimation for directional data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 248-267.
    14. Vincent Douchamps & Matteo Volo & Alessandro Torcini & Demian Battaglia & Romain Goutagny, 2024. "Gamma oscillatory complexity conveys behavioral information in hippocampal networks," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    15. Bedouhene Kahina & Zougab Nabil, 2020. "A Bayesian procedure for bandwidth selection in circular kernel density estimation," Monte Carlo Methods and Applications, De Gruyter, vol. 26(1), pages 69-82, March.
    16. Marczak, Martyna & Gómez, Víctor, 2012. "SPECTRAN, a set of Matlab programs for Spectral analysis," FZID Discussion Papers 60-2012, University of Hohenheim, Center for Research on Innovation and Services (FZID).
    17. Yanhong Wu, 1996. "A less sensitive linear detector for the change point based on kernel smoothing method," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 43(1), pages 43-55, December.
    18. Federico Rocchi & Carola Canella & Shahryar Noei & Daniel Gutierrez-Barragan & Ludovico Coletta & Alberto Galbusera & Alexia Stuefer & Stefano Vassanelli & Massimo Pasqualetti & Giuliano Iurilli & Ste, 2022. "Increased fMRI connectivity upon chemogenetic inhibition of the mouse prefrontal cortex," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    19. Rashid Mehmood & Muhammad Riaz & Ronald Does, 2013. "Efficient power computation for r out of m runs rules schemes," Computational Statistics, Springer, vol. 28(2), pages 667-681, April.
    20. Charles C. Taylor & Kanti V. Mardia & Marco Di Marzio & Agnese Panzera, 2012. "Validating protein structure using kernel density estimates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2379-2388, July.

    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:eee:csdana:v:105:y:2017:i:c:p:268-279. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.