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AIMS Mathematics

2022, Volume 7, Issue 6: 10609-10624. doi: 10.3934/math.2022592
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Research article

Improved generalized class of estimators in estimating the finite population mean using two auxiliary variables under two-stage sampling

  • 1.
    Department of Statistics, Abdul Wali Khan University, Mardan, Pakistan
  • 2.
    Department of Statistics, Quaid-i-azam University, Islamabad, Pakistan
  • 3.
    Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
  • 4.
    Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
  • 5.
    Department of Statistics, Yazd University, P.O. Box 89175-741, Yazd, Iran
  • 6.
    Statistics Department, Faculty of Science, King Abdul Aziz University, Jeddah, Kingdom of Saudi Arabia
  • Received: 18 January 2022 Revised: 13 March 2022 Accepted: 24 March 2022 Published: 29 March 2022

  • MSC : 62D05

  • This article addresses the problem of estimating the finite population mean using two auxiliary variables under two-stage sampling scheme. Further the proposed improved class of estimators are provided their genralized form. Mathematical properties of the existing and proposed improved generalized class of estimators are derived up to first order of approximation. We identified 11 members of the improved generalized class of estimators which are more efficient than existing estimators in terms of the percentage relative efficiency. We use two real data sets under two-stage sampling to compare the performances of all of considered estimators.

    Citation: Sohaib Ahmad, Sardar Hussain, Javid Shabbir, Muhammad Aamir, M. El-Morshedy, Zubair Ahmad, Sharifah Alrajhi. Improved generalized class of estimators in estimating the finite population mean using two auxiliary variables under two-stage sampling[J]. AIMS Mathematics, 2022, 7(6): 10609-10624. doi: 10.3934/math.2022592

    Related Papers:

  • This article addresses the problem of estimating the finite population mean using two auxiliary variables under two-stage sampling scheme. Further the proposed improved class of estimators are provided their genralized form. Mathematical properties of the existing and proposed improved generalized class of estimators are derived up to first order of approximation. We identified 11 members of the improved generalized class of estimators which are more efficient than existing estimators in terms of the percentage relative efficiency. We use two real data sets under two-stage sampling to compare the performances of all of considered estimators.


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