IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v51y2022i20p7038-7055.html
   My bibliography  Save this article

Estimation and prediction based on record statistics in the presence of an outlier

Author

Listed:
  • Bahareh Khatib Astaneh
  • Jafar Ahmadi

Abstract

A single outlier sequence in which the distribution of the first observation differs from the others is considered and the properties of record statistics extracted from such sequence are studied. The problem of estimating the model parameters is discussed in the proportional hazard rate model. The maximum likelihood estimator and the uniformly minimum variance unbiased estimator are obtained for the special case of exponential distribution. The best linear unbiased (invariant) estimator is also derived for the location-scale family of distributions and their efficiencies are calculated. The problems of predicting the future records and reconstructing the past records are investigated. Various predictors and reconstructors are presented and some of their properties are stated. The precision of the obtained predictors are compared based on both the mean squared prediction error and Pitman’s measure of closeness criteria. Finally, an example with real data is given to illustrate the results.

Suggested Citation

  • Bahareh Khatib Astaneh & Jafar Ahmadi, 2022. "Estimation and prediction based on record statistics in the presence of an outlier," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(20), pages 7038-7055, October.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:20:p:7038-7055
    DOI: 10.1080/03610926.2020.1870141
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2020.1870141
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2020.1870141?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:51:y:2022:i:20:p:7038-7055. 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.