IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v46y2017i5p2249-2269.html
   My bibliography  Save this article

Efficient use of multi-auxiliary information in search of good rotation patterns in successive sampling

Author

Listed:
  • Housila P. Singh
  • Manoj K. Srivastava
  • Namita Srivastava

Abstract

This article considers the problem of estimating the population mean on the current (second) occasion using multi-auxiliary information in successive sampling over two occasions. A general class of estimators is proposed for estimating population mean on the current occasion and expressions for bias and mean square error for these estimators are obtained up to first degree of approximation. The minimum variance bound estimator in the proposed class is discussed. Many popular estimators have been shown to belong to this class. Optimum replacement policy is also discussed. Finally, the superiority of the proposed class of estimators over multivariate version of chain type ratio estimator envisaged by Singh (2005) is established empirically.

Suggested Citation

  • Housila P. Singh & Manoj K. Srivastava & Namita Srivastava, 2017. "Efficient use of multi-auxiliary information in search of good rotation patterns in successive sampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(5), pages 2249-2269, March.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:5:p:2249-2269
    DOI: 10.1080/03610926.2015.1041979
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2015.1041979
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2015.1041979?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:46:y:2017:i:5:p:2249-2269. 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.