IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v38y2011i9p1883-1897.html
   My bibliography  Save this article

The analysis of ordinal time-series data via a transition (Markov) model

Author

Listed:
  • Kathryn Bartimote-Aufflick
  • Peter C. Thomson

Abstract

While standard techniques are available for the analysis of time-series (longitudinal) data, and for ordinal (rating) data, not much is available for the combination of the two, at least in a readily-usable form. However, this data type is common place in the natural and health sciences where repeated ratings are recorded on the same subject. To analyse these data, this paper considers a transition (Markov) model where the rating of a subject at one time depends explicitly on the observed rating at the previous point of time by incorporating the previous rating as a predictor variable. Complications arise with adequate handling of data at the first observation ( t =1), as there is no prior observation to use as a predictor. To overcome this, it is postulated the existence of a rating at time t =0; however it is treated as ‘missing data’ and the expectation--maximisation algorithm used to accommodate this. The particular benefits of this method are shown for shorter time series.

Suggested Citation

  • Kathryn Bartimote-Aufflick & Peter C. Thomson, 2011. "The analysis of ordinal time-series data via a transition (Markov) model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1883-1897, September.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1883-1897
    DOI: 10.1080/02664763.2010.529885
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2010.529885
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2010.529885?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. JosE Eduardo Corrente & Maria Del Pilar DIAz, 2003. "Ordinal models and generalized estimating equations to evaluate disease severity," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(4), pages 425-439.
    2. Youngjo Lee, 2007. "Linear and Generalized Linear Models and their Applications by J. JIANG," Biometrics, The International Biometric Society, vol. 63(4), pages 1297-1298, December.
    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. Hatzikyriakou, Adam & Lin, Ning, 2017. "Impact of performance interdependencies on structural vulnerability: A systems perspective of storm surge risk to coastal residential communities," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 106-116.
    2. Jones, Calvin & Jordan, Declan, 2014. "Competitiveness in Soccer Leagues: An ordinal time series approach with evidence from the Premier League 1993 to 2014," MPRA Paper 61193, University Library of Munich, Germany, revised Dec 2014.

    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. Keunbaik Lee & Sanggil Kang & Xuefeng Liu & Daekwan Seo, 2011. "Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1577-1590, July.

    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:taf:japsta:v:38:y:2011:i:9:p:1883-1897. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.