An Improved Data Science Based Ratio-Type Estimator for Population Mean in Stratified Random Sampling Using Multi-Auxiliary Information

Abstract

This article proposed an improved data science ratio-type estimator for estimating the population mean of a study variable using multiple auxiliary variables in stratified sampling. In this paper, we present a comprehensive view on data science including various types of estimating advanced analytics and population methods that can be applied multiple auxiliary and capabilities. The theoretical properties of the proposed estimator are investigated, and its bias and mean square error (MSE) are derived under suitable assumptions. The efficiency of the proposed estimator is compared with that of the estimator proposed by Sharma and Kumar. The relative efficiency of the estimators is examined both theoretically and numerically. A numerical illustration is also presented to demonstrate the performance of the proposed estimator and to assess its efficiency in comparison with the existing estimator. The results indicate the effectiveness of the proposed estimator in improving the precision of population mean estimation in stratified sampling when multiple auxiliary variables.