IDEAS home Printed from https://ideas.repec.org/a/bot/rivsta/v79y2019i3p291-319.html
   My bibliography  Save this article

Comparisons of Methods of Estimation for a New Pareto-type Distribution

Author

Listed:
  • Ali Saadati Nik

    (Department of Statistics, University of Mazandaran)

  • Akbar Asgharzadeh

    (Department of Statistics, University of Mazandaran)

  • Saralees Nadarajah

    (Department of Mathematics, University of Manchester)

Abstract

Bourguignon et al. (2016) introduced a new Pareto-type distribution to model income and reliability data. The aim of this paper is to estimate the parameters of this distribution from both frequentist and Bayesian view points. The maximum likelihood estimates, method of moment estimates, percentile estimates, least square and weighted least square estimates and maximum product of spacing estimates are considered as frequentist estimates. We have also considered the Bayes estimates of the unknown parameters and the associated credible intervals. The Bayes estimates are computed using an importance sampling method. To evaluate the performance of the different estimates, a Monte Carlo simulation study is carried out. Some real life data sets have been analyzed for illustrative purposes.

Suggested Citation

  • Ali Saadati Nik & Akbar Asgharzadeh & Saralees Nadarajah, 2019. "Comparisons of Methods of Estimation for a New Pareto-type Distribution," Statistica, Department of Statistics, University of Bologna, vol. 79(3), pages 291-319.
  • Handle: RePEc:bot:rivsta:v:79:y:2019:i:3:p:291-319
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Essam A. Ahmed & Tariq S. Alshammari & Mohamed S. Eliwa, 2024. "Different Statistical Inference Algorithms for the New Pareto Distribution Based on Type-II Progressively Censored Competing Risk Data with Applications," Mathematics, MDPI, vol. 12(13), pages 1-32, July.
    2. Fanqun Li & Shanran Wei & Mingtao Zhao, 2023. "Bayesian Estimation of a New Pareto-Type Distribution Based on Mixed Gibbs Sampling Algorithm," Mathematics, MDPI, vol. 12(1), pages 1-13, December.
    3. Saadati Nik, A. & Asgharzadeh, A. & Raqab, Mohammad Z., 2021. "Estimation and prediction for a new Pareto-type distribution under progressive type-II censoring," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 508-530.

    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:bot:rivsta:v:79:y:2019:i:3:p:291-319. 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: Giovanna Galatà (email available below). General contact details of provider: https://edirc.repec.org/data/dsbolit.html .

    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.