IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0275875.html
   My bibliography  Save this article

A simulation study: Using dual ancillary variable to estimate population mean under stratified random sampling

Author

Listed:
  • Sohaib Ahmad
  • Sardar Hussain
  • Uzma Yasmeen
  • Muhammad Aamir
  • Javid Shabbir
  • M El-Morshedy
  • Afrah Al-Bossly
  • Zubair Ahmad

Abstract

In this paper, we propose an improved ratio-in-regression type estimator for the finite population mean under stratified random sampling, by using the ancillary varaible as well as rank of the ancillary varaible. Expressions of the bias and mean square error of the estimators are derived up to the first order of approximation. The present work focused on proper use of the ancillary variable, and it was discussed how ancillary variable can improve the precision of the estimates. Two real data sets as well as simulation study are carried out to observe the performances of the estimators. We demonstrate theoretically and numerically that proposed estimator performs well as compared to all existing estimators.

Suggested Citation

  • Sohaib Ahmad & Sardar Hussain & Uzma Yasmeen & Muhammad Aamir & Javid Shabbir & M El-Morshedy & Afrah Al-Bossly & Zubair Ahmad, 2022. "A simulation study: Using dual ancillary variable to estimate population mean under stratified random sampling," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-19, November.
    • Handle: RePEc:plo:pone00:0275875
    DOI: 10.1371/journal.pone.0275875
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0275875
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0275875&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0275875?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
    ---><---

    References listed on IDEAS

    as
    1. Maria Javed & Muhammad Irfan & Sajjad Haider Bhatti & Ronald Onyango & Niansheng Tang, 2021. "A Simulation-Based Study for Progressive Estimation of Population Mean through Traditional and Nontraditional Measures in Stratified Random Sampling," Journal of Mathematics, Hindawi, vol. 2021, pages 1-16, December.
    2. Nursel Koyuncu & Cem Kadilar, 2010. "On improvement in estimating population mean in stratified random sampling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 999-1013.
    3. Erum Zahid & Javid Shabbir & Sat Gupta & Ronald Onyango & Sadia Saeed, 2022. "A generalized class of estimators for sensitive variable in the presence of measurement error and non-response," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-19, January.
    4. Tolga Zaman, 2021. "An efficient exponential estimator of the mean under stratified random sampling," Mathematical Population Studies, Taylor & Francis Journals, vol. 28(2), pages 104-121, April.
    5. Sardar Hussain & Sohaib Ahmad & Mariyam Saleem & Sohail Akhtar, 2020. "Finite population distribution function estimation with dual use of auxiliary information under simple and stratified random sampling," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-30, September.
    6. Rohini Yadav & Rajesh Tailor, 2020. "Estimation of finite population mean using two auxiliary variables under stratified random sampling," Statistics in Transition New Series, Polish Statistical Association, vol. 21(1), pages 1-12, March.
    Full references (including those not matched with items on IDEAS)

    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. Kanwal Iqbal & Syed Muhammad Muslim Raza & Tahir Mahmood & Muhammad Riaz, 2024. "Exploring mixture estimators in stratified random sampling," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-21, September.
    2. Shashi Bhushan & Anoop Kumar & Usman Shahzad & Amer Ibrahim Al-Omari & Ibrahim Mufrah Almanjahie, 2022. "On Some Improved Class of Estimators by Using Stratified Ranked Set Sampling," Mathematics, MDPI, vol. 10(18), pages 1-32, September.
    3. Giancarlo Diana & Marco Giordan & Pier Perri, 2011. "An improved class of estimators for the population mean," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(2), pages 123-140, June.
    4. Ayesha Khalid & Aamir Sanaullah & Mohammed M. A. Almazah & Fuad S. Al-Duais, 2023. "An Efficient Ratio-Cum-Exponential Estimator for Estimating the Population Distribution Function in the Existence of Non-Response Using an SRS Design," Mathematics, MDPI, vol. 11(6), pages 1-15, March.
    5. Housila Singh & Ramkrishna Solanki, 2013. "A new procedure for variance estimation in simple random sampling using auxiliary information," Statistical Papers, Springer, vol. 54(2), pages 479-497, May.
    6. Sohaib Ahmad & Sardar Hussain & Kalim Ullah & Erum Zahid & Muhammad Aamir & Javid Shabbir & Zubair Ahmad & Huda M Alshanbari & Wejdan Alajlan, 2022. "A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-14, November.
    7. Subhash Kumar Yadav & Mukesh Kumar Verma & Rahul Varshney, 2025. "Optimal Strategy for Elevated Estimation of Population Mean in Stratified Random Sampling under Linear Cost Function," Annals of Data Science, Springer, vol. 12(2), pages 517-538, April.
    8. Lakshmi N. Upadhyaya & Rohini Yadav & Housila P. Singh & S. Chatterjee, 2011. "Improved separate ratio exponential estimator for population mean using auxiliary information," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 12(2), pages 401-412, October.

    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:plo:pone00:0275875. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.