IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v8y2017i1d10.1007_s13198-014-0296-4.html
   My bibliography  Save this article

On estimation of two-component mixture inverse Lomax model via Bayesian approach

Author

Listed:
  • Jafer Rahman

    (Hazara University)

  • Muhammad Aslam

    (Riphah International University)

Abstract

A mixture distribution is the probability distribution of observations in the pooled population which is used to make statistical inferences about the characteristics of the sub-populations on the basis of sample data from the joint population. This article comprises such sort of study for unknown parameters of two-component mixture inverse Lomax distribution based on Bayesian thoughts. Bayes estimators and Bayes posterior risks for the parameters are derived under various loss functions along with the use of conjugate priors. Numerical results for Bayes estimates and Bayes risks are obtained by simulation as well as real data. The study also includes Maximum likelihood estimation for the comparisons with Bayesian estimation.

Suggested Citation

  • Jafer Rahman & Muhammad Aslam, 2017. "On estimation of two-component mixture inverse Lomax model via Bayesian approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(1), pages 99-109, January.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:1:d:10.1007_s13198-014-0296-4
    DOI: 10.1007/s13198-014-0296-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-014-0296-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-014-0296-4?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.

    References listed on IDEAS

    as
    1. Syed Mohsin Ali Kazmi & Muhammad Aslam & Sajid Ali & Nasir Abbas, 2013. "Selection of suitable prior for the Bayesian mixture of a class of lifetime distributions under type-I censored datasets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1639-1658, August.
    2. Kleiber, Christian, 2002. "Lorenz ordering of order statistics from log-logistic and related distributions," Technical Reports 2002,09, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. M. Saleem & M. Aslam & P. Economou, 2010. "On the Bayesian analysis of the mixture of power function distribution using the complete and the censored sample," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(1), pages 25-40.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hanieh Panahi, 2019. "Estimation for the parameters of the Burr Type XII distribution under doubly censored sample with application to microfluidics data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 510-518, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nadeem Akhtar & Sajjad Ahmad Khan & Emad A. A. Ismail & Fuad A. Awwad & Akbar Ali Khan & Taza Gul & Haifa Alqahtani, 2024. "Analyzing quantitative performance: Bayesian estimation of 3-component mixture geometric distributions based on Kumaraswamy prior," Statistical Papers, Springer, vol. 65(7), pages 4431-4451, September.
    2. Sajid Ali & Muhammad Riaz, 2014. "On the generalized process capability under simple and mixture models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(4), pages 832-852, April.
    3. Kleiber, Christian, 2005. "The Lorenz curve in economics and econometrics," Technical Reports 2005,30, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    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:spr:ijsaem:v:8:y:2017:i:1:d:10.1007_s13198-014-0296-4. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.