IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v40y2019i5p631-648.html
   My bibliography  Save this article

Bayesian Outlier Detection in Non‐Gaussian Autoregressive Time Series

Author

Listed:
  • Maria Eduarda Silva
  • Isabel Pereira
  • Brendan McCabe

Abstract

This work investigates outlier detection and modelling in non‐Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference based on the data. The Bayesian approach proposed here allows one to estimate, at each time point, the probability of an outlier occurrence and its corresponding size thus identifying the observations that require further investigation. The methodology is illustrated using simulated and observed data sets.

Suggested Citation

  • Maria Eduarda Silva & Isabel Pereira & Brendan McCabe, 2019. "Bayesian Outlier Detection in Non‐Gaussian Autoregressive Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(5), pages 631-648, September.
    • Handle: RePEc:bla:jtsera:v:40:y:2019:i:5:p:631-648
    DOI: 10.1111/jtsa.12439
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jtsa.12439
    Download Restriction: no
    File URL: https://libkey.io/10.1111/jtsa.12439?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
    ---><---

    Citations

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


    Cited by:

    1. Paolo Gorgi, 2020. "Beta–negative binomial auto‐regressions for modelling integer‐valued time series with extreme observations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1325-1347, December.

    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:40:y:2019:i:5:p:631-648. 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.