IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v38y2011i9p1995-2007.html
   My bibliography  Save this article

Spatio-temporal modeling and prediction of CO concentrations in Tehran city

Author

Listed:
  • Firoozeh Rivaz
  • Mohsen Mohammadzadeh
  • Majid Jafari Khaledi

Abstract

One of the most important agents responsible for high pollution in Tehran is carbon monoxide. Prediction of carbon monoxide is of immense help for sustaining the inhabitants’ health level. In this paper, motivated by the statistical analysis of carbon monoxide using the empirical Bayes approach, we deal with the issue of prior specification for the model parameters. In fact, the hyperparameters (the parameters of the prior law) are estimated based on a sampling-based method which depends only on the specification of the marginal spatial and temporal correlation structures. We compare the predictive performance of this approach with the type II maximum likelihood method. Results indicate that the proposed procedure performs better for this data set.

Suggested Citation

  • Firoozeh Rivaz & Mohsen Mohammadzadeh & Majid Jafari Khaledi, 2011. "Spatio-temporal modeling and prediction of CO concentrations in Tehran city," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1995-2007, November.
  • Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1995-2007
    DOI: 10.1080/02664763.2010.545108
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2010.545108
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    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:1995-2007. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Longhurst). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.