IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v16y2020i1p18n3.html
   My bibliography  Save this article

Bayesian Detection of Piecewise Linear Trends in Replicated Time-Series with Application to Growth Data Modelling

Author

Listed:
  • Papastamoulis Panagiotis

    (Department of Statistics, School of Information Sciences and Technology, Athens University of Economics and Business, Patision 76, 104 34Athens, Greece)

  • Furukawa Takanori
  • van Rhijn Norman
  • Bromley Michael
  • Bignell Elaine

    (Division of Infection, Immunity & Respiratory Medicine, Faculty of Biology, Medicine & Health, University of Manchester, Manchester, UK)

  • Rattray Magnus

    (Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PL, UK)

Abstract

We consider the situation where a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. We develop a Bayesian approach to infer the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence. A Metropolis-Hastings Markov chain Monte Carlo (MCMC) sampler is constructed for approximating the posterior distribution. Our method is benchmarked using simulated data and is applied to uncover differences in the dynamics of fungal growth from imaging time course data collected from different strains. The source code is available on CRAN.

Suggested Citation

  • Papastamoulis Panagiotis & Furukawa Takanori & van Rhijn Norman & Bromley Michael & Bignell Elaine & Rattray Magnus, 2020. "Bayesian Detection of Piecewise Linear Trends in Replicated Time-Series with Application to Growth Data Modelling," The International Journal of Biostatistics, De Gruyter, vol. 16(1), pages 1-18, May.
    • Handle: RePEc:bpj:ijbist:v:16:y:2020:i:1:p:18:n:3
    DOI: 10.1515/ijb-2018-0052
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2018-0052
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/ijb-2018-0052?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.

    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:bpj:ijbist:v:16:y:2020:i:1:p:18:n:3. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.