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

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