IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v32y2011i5p498-511.html
   My bibliography  Save this article

Mean shift testing in correlated data

Author

Listed:
  • Michael Robbins
  • Colin Gallagher
  • Robert Lund
  • Alexander Aue

Abstract

No abstract is available for this item.

Suggested Citation

  • Michael Robbins & Colin Gallagher & Robert Lund & Alexander Aue, 2011. "Mean shift testing in correlated data," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(5), pages 498-511, September.
    • Handle: RePEc:bla:jtsera:v:32:y:2011:i:5:p:498-511
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Jie Shen & Colin M. Gallagher & QiQi Lu, 2014. "Detection of multiple undocumented change-points using adaptive Lasso," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1161-1173, June.
    2. Shen, Gang, 2013. "On empirical likelihood inference of a change-point," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1662-1668.
    3. Cho, Haeran & Fryzlewicz, Piotr, 2023. "Multiple change point detection under serial dependence: wild contrast maximisation and gappy Schwarz algorithm," LSE Research Online Documents on Economics 120085, London School of Economics and Political Science, LSE Library.
    4. Michael W. Robbins & Colin M. Gallagher & Robert B. Lund, 2016. "A General Regression Changepoint Test for Time Series Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 670-683, April.
    5. Song, Junmo & Baek, Changryong, 2019. "Detecting structural breaks in realized volatility," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 58-75.
    6. Christopher Dienes & Alexander Aue, 2014. "On-Line Monitoring Of Pollution Concentrations With Autoregressive Moving Average Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(3), pages 239-261, May.
    7. Mo Li & QiQi Lu, 2022. "Changepoint detection in autocorrelated ordinal categorical time series," Environmetrics, John Wiley & Sons, Ltd., vol. 33(7), November.
    8. Byungsoo Kim & Junmo Song & Changryong Baek, 2021. "Robust test for structural instability in dynamic factor models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(4), pages 821-853, August.
    9. Song, Junmo & Kang, Jiwon, 2018. "Parameter change tests for ARMA–GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 41-56.
    10. Shi, Xuesheng & Gallagher, Colin & Lund, Robert & Killick, Rebecca, 2022. "A comparison of single and multiple changepoint techniques for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
    11. Yunwei Cui & Rongning Wu & Qi Zheng, 2021. "Estimation of change‐point for a class of count time series models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1277-1313, December.
    12. Robert Lund & Gang Liu & Qin Shao, 2016. "A New Approach to ANOVA Methods for Autocorrelated Data," The American Statistician, Taylor & Francis Journals, vol. 70(1), pages 55-62, February.
    13. Gianluca Mastrantonio & Giovanna Jona Lasinio & Alessio Pollice & Lorenzo Teodonio & Giulia Capotorti, 2022. "A Dirichlet process model for change‐point detection with multivariate bioclimatic data," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.

    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:bla:jtsera:v:32:y:2011:i:5:p:498-511. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 .

    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.