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Estimation of Coefficient of Variation Using Calibrated Estimators in Double Stratified Random Sampling

Author

Listed:
  • Usman Shahzad

    (Department of Mathematics and Statistics, International Islamic University, Islamabad 44000, Pakistan
    Department of Mathematics and Statistics, PMAS Arid Agriculture University, Rawalpindi 46300, Pakistan)

  • Ishfaq Ahmad

    (Department of Mathematics and Statistics, International Islamic University, Islamabad 44000, Pakistan)

  • Amelia V. García-Luengo

    (Department of Mathematics, University of Almeria, 04120 Almeria, Spain)

  • Tolga Zaman

    (Department of Statistics, Faculty of Science, Çankiri Karatekin University, Çankiri 18100, Turkey)

  • Nadia H. Al-Noor

    (Department of Mathematics, College of Science, Mustansiriyah University, Baghdad 10011, Iraq)

  • Anoop Kumar

    (Department of Statistics, Amity University, Lucknow 226028, Uttar Pradesh, India)

Abstract

One of the most useful indicators of relative dispersion is the coefficient of variation. The characteristics of the coefficient of variation have contributed to its widespread use in most scientific and academic disciplines, with real life applications. The traditional estimators of the coefficient of variation are based on conventional moments; therefore, these are highly affected by the presence of extreme values. In this article, we develop some novel calibration-based coefficient of variation estimators for the study variable under double stratified random sampling (DSRS) using the robust features of linear (L and TL) moments, which offer appropriate coefficient of variation estimates. To evaluate the usefulness of the proposed estimators, a simulation study is performed by using three populations out of which one is based on the COVID-19 pandemic data set and the other two are based on apple fruit data sets. The relative efficiency of the proposed estimators with respect to the existing estimators has been calculated. The superiority of the suggested estimators over the existing estimators are clearly validated by using the real data sets.

Suggested Citation

  • Usman Shahzad & Ishfaq Ahmad & Amelia V. García-Luengo & Tolga Zaman & Nadia H. Al-Noor & Anoop Kumar, 2023. "Estimation of Coefficient of Variation Using Calibrated Estimators in Double Stratified Random Sampling," Mathematics, MDPI, vol. 11(1), pages 1-17, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:1:p:252-:d:1024078
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    References listed on IDEAS

    as
    1. Zaman, Tolga, 2019. "Improvement of modified ratio estimators using robust regression methods," Applied Mathematics and Computation, Elsevier, vol. 348(C), pages 627-631.
    2. Usman Shahzad & Nadia H. Al-Noor & Muhammad Hanif & Irsa Sajjad, 2021. "An exponential family of median based estimators for mean estimation with simple random sampling scheme," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(20), pages 4890-4899, September.
    3. Mahmoudvand, Rahim & Hassani, Hossein & Wilson, Rob, 2007. "Is The Sample Coefficient Of Variation A Good Estimator For The Population Coefficient Of Variation?," MPRA Paper 6106, University Library of Munich, Germany, revised 2007.
    4. Nursel Koyuncu, 2018. "Calibration estimator of population mean under stratified ranked set sampling design," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(23), pages 5845-5853, December.
    5. Tolga Zaman & Hasan Bulut, 2019. "Modified ratio estimators using robust regression methods," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(8), pages 2039-2048, April.
    Full references (including those not matched with items on IDEAS)

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