IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v40y2013i9p2069-2086.html
   My bibliography  Save this article

Identifying radon-prone building typologies by marginal modelling

Author

Listed:
  • Riccardo Borgoni
  • Valeria Tritto
  • Daniela de Bartolo

Abstract

Radon is a naturally occurring decay product of uranium known to be the main contributor to natural background radiation exposure. It has been established that the health risk related to radon exposure is lung cancer. In fact, radon is considered to be a major leading cause of lung cancer, second only to smoking. In this paper, we identified building typologies that affect the probability of detecting indoor radon concentration above reference values, using the data collected within two monitoring campaigns recently conducted in Northern Italy. This information is fundamental both in prevention, i.e. when the construction of a new building is planned and in mitigation, i.e. when a high concentration detected inside buildings has to be reduced. A spatial regression approach for binary data was adopted for this goal where some relevant covariates on the soil were retrieved by linking external spatial databases.

Suggested Citation

  • Riccardo Borgoni & Valeria Tritto & Daniela de Bartolo, 2013. "Identifying radon-prone building typologies by marginal modelling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(9), pages 2069-2086, September.
  • Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:2069-2086
    DOI: 10.1080/02664763.2013.804906
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2013.804906
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/02664763.2013.804906?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. Riccardo Borgoni & Ann Berrington & Peter Smith, 2012. "Selecting and fitting graphical chain models to longitudinal data," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(3), pages 715-738, April.
    2. Riccardo Borgoni & Piero Quatto & Giorgio Somà & Daniela Bartolo, 2010. "A geostatistical approach to define guidelines for radon prone area identification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(2), pages 255-276, June.
    3. Rob Foxall & Adrian Baddeley, 2002. "Nonparametric measures of association between a spatial point process and a random set, with geological applications," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(2), pages 165-182, May.
    Full references (including those not matched with items on IDEAS)

    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. Riccardo Borgoni & Ann Berrington, 2013. "Evaluating a sequential tree-based procedure for multivariate imputation of complex missing data structures," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 1991-2008, June.
    2. Riccardo Borgoni & Valeria Tritto & Carlo Bigliotto & Daniela De Bartolo, 2011. "A Geostatistical Approach to Assess the Spatial Association between Indoor Radon Concentration, Geological Features and Building Characteristics: The Case of Lombardy, Northern Italy," IJERPH, MDPI, vol. 8(5), pages 1-21, May.
    3. Borrajo, M.I. & González-Manteiga, W. & Martínez-Miranda, M.D., 2024. "Goodness-of-fit test for point processes first-order intensity," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    4. M. Lieshout, 2006. "A J-Function for Marked Point Patterns," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(2), pages 235-259, June.

    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:40:y:2013:i:9:p:2069-2086. 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.