IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i24p4700-d1000249.html
   My bibliography  Save this article

Improved EDF-Based Tests for Weibull Distribution Using Ranked Set Sampling

Author

Listed:
  • Safar M. Alghamdi

    (Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Rashad A. R. Bantan

    (Department of Marine Geology, Faculty of Marine Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia)

  • Amal S. Hassan

    (Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt)

  • Heba F. Nagy

    (Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt)

  • Ibrahim Elbatal

    (Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt
    Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Mohammed Elgarhy

    (The Higher Institute of Commercial Sciences, Al Mahalla Al Kubra 31951, Egypt)

Abstract

It is well known that ranked set sampling (RSS) is superior to conventional simple random sampling (SRS) in that it frequently results in more effective inference techniques. One of the most popular and broadly applicable models for lifetime data is the Weibull distribution. This article proposes different modified goodness-of-fit tests based on the empirical distribution function (EDF) for the Weibull distribution. The recommended RSS tests are compared to their SRS counterparts. For each scheme, the critical values of the relevant test statistics are computed. A comparison of the power of the suggested goodness-of-fit tests based on a number of alternatives is investigated. RSS tests are more effective than their SRS equivalents, according to simulated data.

Suggested Citation

  • Safar M. Alghamdi & Rashad A. R. Bantan & Amal S. Hassan & Heba F. Nagy & Ibrahim Elbatal & Mohammed Elgarhy, 2022. "Improved EDF-Based Tests for Weibull Distribution Using Ranked Set Sampling," Mathematics, MDPI, vol. 10(24), pages 1-24, December.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4700-:d:1000249
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/24/4700/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/24/4700/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heba F. Nagy & Amer Ibrahim Al-Omari & Amal S. Hassan & Ghadah A. Alomani, 2022. "Improved Estimation of the Inverted Kumaraswamy Distribution Parameters Based on Ranked Set Sampling with an Application to Real Data," Mathematics, MDPI, vol. 10(21), pages 1-19, November.
    2. Abdullah M Almarashi & Ali Algarni & Mazen Nassar, 2020. "On estimation procedures of stress-strength reliability for Weibull distribution with application," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-23, August.
    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. Amal S. Hassan & Ibrahim M. Almanjahie & Amer Ibrahim Al-Omari & Loai Alzoubi & Heba Fathy Nagy, 2023. "Stress–Strength Modeling Using Median-Ranked Set Sampling: Estimation, Simulation, and Application," Mathematics, MDPI, vol. 11(2), pages 1-19, January.

    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. Jorge Figueroa-Zúñiga & Juan G. Toledo & Bernardo Lagos-Alvarez & Víctor Leiva & Jean P. Navarrete, 2023. "Inference Based on the Stochastic Expectation Maximization Algorithm in a Kumaraswamy Model with an Application to COVID-19 Cases in Chile," Mathematics, MDPI, vol. 11(13), pages 1-14, June.

    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:gam:jmathe:v:10:y:2022:i:24:p:4700-:d:1000249. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.