Improved Mean Estimators for Population utilizing Dual Supplementary Characteristics under Simple Random Sampling

Abstract

This paper makes another addition to the existing literature of population mean estimation. An improved family of mean estimators for the population is suggested using simple random sampling and utilizing the information of dual supplementary characteristics. We have conducted an extensive theoretical and numerical investigation of these recommended estimators using the criteria of bias and mean square error. The study provides a derivation of the bias and mean square error of the recommended estimators, approximating up to the first order and compared with the existing estimators. The suggested estimators are also extensively studied numerically and compared using real-world data sets and simulation studies. The theoretical and numerical comparisons show that the estimators suggested in the study are much more efficient than the competitor estimators under all situations i.e. Bahl and Tuteja [21] and other existing estimators in the literature. Therefore, the suggested family of estimators can be applied to real-life problems to obtain better results than the existing mean estimators for the population.