IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v69y2020i2p277-300.html
   My bibliography  Save this article

Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives

Author

Listed:
  • Vincent Chin
  • David Gunawan
  • Denzil G. Fiebig
  • Robert Kohn
  • Scott A. Sisson

Abstract

The paper considers the problem of estimating a multivariate probit model in a panel data setting with emphasis on sampling a high dimensional correlation matrix and improving the overall efficiency of the data augmentation approach. We reparameterize the correlation matrix in a principled way and then carry out efficient Bayesian inference by using Hamiltonian Monte Carlo sampling. We also propose a novel antithetic variable method to generate samples from the posterior distribution of the random effects and regression coefficients, resulting in significant gains in efficiency. We apply the methodology by analysing stated preference data obtained from Australian general practitioners evaluating alternative contraceptive products. Our analysis suggests that the joint probability of discussing combinations of contraceptive products with a patient shows medical practice variation among the general practitioners, which indicates some resistance even to discuss these products, let alone to recommend them.

Suggested Citation

  • Vincent Chin & David Gunawan & Denzil G. Fiebig & Robert Kohn & Scott A. Sisson, 2020. "Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(2), pages 277-300, April.
  • Handle: RePEc:bla:jorssc:v:69:y:2020:i:2:p:277-300
    DOI: 10.1111/rssc.12393
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssc.12393
    Download Restriction: no

    File URL: https://libkey.io/10.1111/rssc.12393?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
    ---><---

    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:jorssc:v:69:y:2020:i:2:p:277-300. 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: https://edirc.repec.org/data/rssssea.html .

    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.