IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v49y2022i13p3451-3476.html
   My bibliography  Save this article

Inverse Lindley power series distributions: a new compounding family and regression model with censored data

Author

Listed:
  • Mohammed K. Shakhatreh
  • Sanku Dey
  • Devendra Kumar

Abstract

This paper introduces a new class of distributions by compounding the inverse Lindley distribution and power series distributions which is called compound inverse Lindley power series (CILPS) distributions. An important feature of this distribution is that the lifetime of the component associated with a particular risk is not observable, rather only the minimum lifetime value among all risks is observable. Further, these distributions exhibit an unimodal failure rate. Various properties of the distribution are derived. Besides, two special models of the new family are investigated. The model parameters of the two sub-models of the new family are obtained by the methods of maximum likelihood, least square, weighted least square and maximum product of spacing and compared them using the Monte Carlo simulation study. Besides, the log compound inverse Lindley regression model for censored data is proposed. Three real data sets are analyzed to illustrate the flexibility and importance of the proposed models.

Suggested Citation

  • Mohammed K. Shakhatreh & Sanku Dey & Devendra Kumar, 2022. "Inverse Lindley power series distributions: a new compounding family and regression model with censored data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(13), pages 3451-3476, October.
    • Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3451-3476
    DOI: 10.1080/02664763.2021.1951683
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2021.1951683
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2021.1951683?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:japsta:v:49:y:2022:i:13:p:3451-3476. 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/CJAS20 .

    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.