IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v46y2017i15p7301-7312.html
   My bibliography  Save this article

On a Skew Bimodal Normal–Normal distribution fitted to the Old-Faithful geyser data

Author

Listed:
  • Sayed Mohammad Reza Alavi
  • Mahtab Tarhani

Abstract

The Bimodal Normal distribution introduced by Alavi (2011) is a symmetric distribution where its variance is three times the variance of the corresponding normal distribution. Azzalini (1985) introduced the univariate Skew Normal distribution to model asymmetry data. In this paper the Skew Bimodal Normal–Normal distribution is introduced as a skew-symmetric distribution generated by the cumulative function of standard normal. Some properties of the distribution and some methods for generating data from this distribution are introduced. The maximum likelihood estimation of parameters is obtained. The distribution is fitted to the Old Faithful Geyser data.

Suggested Citation

  • Sayed Mohammad Reza Alavi & Mahtab Tarhani, 2017. "On a Skew Bimodal Normal–Normal distribution fitted to the Old-Faithful geyser data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(15), pages 7301-7312, August.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:15:p:7301-7312
    DOI: 10.1080/03610926.2016.1148731
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2016.1148731
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2016.1148731?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:15:p:7301-7312. 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.