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

Usman Shahzad, Ishfaq Ahmad, Amelia V. García-Luengo (), Tolga Zaman, Nadia H. Al-Noor and Anoop Kumar ()
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Usman Shahzad: Department of Mathematics and Statistics, International Islamic University, Islamabad 44000, 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

Mathematics, 2023, vol. 11, issue 1, 1-17

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.

Keywords: coefficient of variation; linear moments; calibration approach; double stratified random sampling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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