Comparison of Some Calibration Estimators for Estimating the Average Wheat Production in Iraqi Governorates Based on the Cultivated Area

Abstract

This study presents a set of calibration estimators to estimate the population mean under stratified random sampling using a single auxiliary variable. A numerical illustration is provided to demonstrate the implementation procedure and computational aspects of these estimators. Furthermore, their performance is evaluated and compared with that of the traditional estimator. The empirical results indicate that applying the calibration technique in estimating the population mean significantly enhances the precision of the estimates and reduces the variance compared to the conventional approach. Among the four estimators examined, the Koyuncu and Kadilar method yielded the lowest estimation variance, making it the most efficient estimator. Meanwhile, the Alam and Singh estimators exhibited comparable accuracy, showing substantial improvement over the traditional estimator while maintaining stable adjusted weights close to the original ones. These findings provide strong evidence of the effectiveness of the calibration approach and highlight its importance in improving the reliability of statistical data used for planning and decision-making processes