IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v48y2019i11p2766-2786.html
   My bibliography  Save this article

Revisit of a randomized response model for estimating a rare sensitive attribute under probability proportional to size sampling using Poisson probability distribution

Author

Listed:
  • G. N. Singh
  • C. Singh
  • S. Suman

Abstract

The present article deals with the estimation of mean number of individuals possess a rare sensitive attribute using Poisson probability distribution, when the population consists of clusters. Unbiased estimation procedures for the mean number of individuals have been suggested and their properties are discussed when the parameter of a rare non-sensitive unrelated attribute is assumed to be known as well as unknown. The suggested estimation procedure is further discussed for situation of stratified cluster population. Empirical studies are carried out to show the dominance of proposed method and resultant estimators over a well-known contemporary estimator.

Suggested Citation

  • G. N. Singh & C. Singh & S. Suman, 2019. "Revisit of a randomized response model for estimating a rare sensitive attribute under probability proportional to size sampling using Poisson probability distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(11), pages 2766-2786, June.
  • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:11:p:2766-2786
    DOI: 10.1080/03610926.2018.1508718
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2018.1508718
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03610926.2018.1508718?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.

    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:lstaxx:v:48:y:2019:i:11:p:2766-2786. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.