IDEAS home Printed from https://ideas.repec.org/p/rtr/wpaper/0054.html
   My bibliography  Save this paper

Model assisted approaches to complex survey sampling from finite populations using Bayesian Networks

Author

Listed:
  • Marco Ballin
  • Mauro Scanu
  • Paola Vicard

Abstract

A class of estimators based on the dependency structure of a multivariate variable of interest and the survey design is defined. The dependency structure is the one described by the Bayesian networks. This class allows ratio type estimators as a subclass identified by a particular dependency structure. It will be shown by a Monte Carlo simulation how the adoption of the estimator corresponding to the population structure is more efficient than the others. It will also be underlined how this class adapts to the problem of integration of information from two surveys through probability updating system of the Bayesian networks.

Suggested Citation

  • Marco Ballin & Mauro Scanu & Paola Vicard, 2005. "Model assisted approaches to complex survey sampling from finite populations using Bayesian Networks," Departmental Working Papers of Economics - University 'Roma Tre' 0054, Department of Economics - University Roma Tre.
    • Handle: RePEc:rtr:wpaper:0054
    as

    Download full text from publisher

    File URL: http://host.uniroma3.it/dipartimenti/economia/pdf/wp54.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Graphical models; probability update; survey design;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:rtr:wpaper:0054. 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: Telephone for information (email available below). General contact details of provider: https://edirc.repec.org/data/dero3it.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.