IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v57y2001i1p203-210.html
   My bibliography  Save this article

Combining Datasets to Predict the Effects of Regulation of Environmental Lead Exposure in Housing Stock

Author

Listed:
  • Warren J. Straws
  • Raymond J. Carroll
  • Steven M. Bortnick
  • John R. Menkedick
  • Bradley D. Schultz

Abstract

Summary. A model for children's blood lead concentrations as a function of environmental lead exposures was developed by combining two nationally representative sources of data that characterize the marginal distributions of blood lead and environmental lead with a third regional dataset that contains joint measures of blood lead and environmental lead. The complicating factor addressed in this article was the fact that methods for assessing environmental lead were different in the national and regional datasets. Relying on an assumption of transportability (that although the marginal distributions of blood lead and environmental lead may be different between the regional dataset and the nation as a whole, the joint relationship between blood lead and environmental lead is the same), the model makes use of a latent variable approach to estimate the joint distribution of blood lead and environmental lead nationwide.

Suggested Citation

  • Warren J. Straws & Raymond J. Carroll & Steven M. Bortnick & John R. Menkedick & Bradley D. Schultz, 2001. "Combining Datasets to Predict the Effects of Regulation of Environmental Lead Exposure in Housing Stock," Biometrics, The International Biometric Society, vol. 57(1), pages 203-210, March.
    • Handle: RePEc:bla:biomet:v:57:y:2001:i:1:p:203-210
    DOI: 10.1111/j.0006-341X.2001.00203.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.0006-341X.2001.00203.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.0006-341X.2001.00203.x?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luo, Guowang & Wu, Mixia & Pang, Zhen, 2022. "Estimation of spatial autoregressive models with covariate measurement errors," Journal of Multivariate Analysis, Elsevier, vol. 192(C).

    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:biomet:v:57:y:2001:i:1:p:203-210. 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: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.