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AIMS Mathematics

2022, Volume 7, Issue 3: 4592-4613. doi: 10.3934/math.2022256
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Research article

Estimation of finite population mean using dual auxiliary variable for non-response using simple random sampling

  • 1.
    Department of Statistics, Abdul Wali Khan University, Mardan, Pakistan
  • 2.
    Department of Statistics, Quaid-i-azam University, Islamabad, Pakistan
  • 3.
    PIDE School of Economics, Pakistan Institute of Development Economics, Islamabad, Pakistan
  • 4.
    Departments of Mathematics, College of Science and Humanities in Al-kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
  • 5.
    Department of Mathematics, College of Science and Arts, Qassim University, Ar Rass, Saudi Arabia
  • Received: 15 July 2021 Revised: 10 December 2021 Accepted: 14 December 2021 Published: 23 December 2021

  • MSC : 65D05

  • This paper addresses the issue of estimating the population mean for non-response using simple random sampling. A new family of estimators is proposed for estimating the population mean with auxiliary information on the sample mean and the rank of the auxiliary variable. Bias and mean square errors of existing and proposed estimators are obtained using the first order of measurement. Theoretical comparisons are made of the performance of the proposed and existing estimators. We show that the proposed family of estimators is more efficient than existing estimators in the literature under the given constraints using these theoretical comparisons.

    Citation: Sohaib Ahmad, Sardar Hussain, Muhammad Aamir, Faridoon Khan, Mohammed N Alshahrani, Mohammed Alqawba. Estimation of finite population mean using dual auxiliary variable for non-response using simple random sampling[J]. AIMS Mathematics, 2022, 7(3): 4592-4613. doi: 10.3934/math.2022256

    Related Papers:

  • This paper addresses the issue of estimating the population mean for non-response using simple random sampling. A new family of estimators is proposed for estimating the population mean with auxiliary information on the sample mean and the rank of the auxiliary variable. Bias and mean square errors of existing and proposed estimators are obtained using the first order of measurement. Theoretical comparisons are made of the performance of the proposed and existing estimators. We show that the proposed family of estimators is more efficient than existing estimators in the literature under the given constraints using these theoretical comparisons.


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  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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