IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v1y1982i2p89-96.html
   My bibliography  Save this article

Estimation of a mean in the presence of an extreme observation

Author

Listed:
  • Feuerverger, A.
  • Guttman, I.
  • Sinha, S. K.

Abstract

The performance of Anscombe, semi-Winsorization and Winsorization (A, S and W) rules for dealing with extreme observations are investigated for observations from N([mu], [sigma]2) and the simple case where it is assumed that at most one observation in the sample may be biased, arising from N([mu] + a[sigma], [sigma]2) and the primary objective is to estimate [mu] when [sigma] is unknown. Each of these rules is separately treated in terms of the estimated standard deviation, range and interquartile range. A Monte Carlo method is used to evaluate certain expectation integrals that arise in the computations. We give the results for sample sizes n = 6, 8, 10, 12, 14, 16, 20, 30, 40, 50, 60, 80, 100 of determining the constants necessary to give 'premiums' of 0.01 and 0.05 for each of the rules. The performance of the rules is measured in terms of 'protection'. Features of the resulting tables are discussed.

Suggested Citation

  • Feuerverger, A. & Guttman, I. & Sinha, S. K., 1982. "Estimation of a mean in the presence of an extreme observation," Statistics & Probability Letters, Elsevier, vol. 1(2), pages 89-96, November.
    • Handle: RePEc:eee:stapro:v:1:y:1982:i:2:p:89-96
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0167-7152(82)90022-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:stapro:v:1:y:1982:i:2:p:89-96. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.