Computation strategy for population mean in stratified random sampling: A problem of budget constraint

In this study, we utilize the characteristics of one auxiliary variable to obtain the population mean using proposed estimators in stratified random sampling. By examining the MSE and PRE of the proposed estimator and the existing estimator, we compare the effectiveness of the proposed estimator with each of these existing estimators. Additionally, use a linear cost function and a non-linear cost function to find the minimum mean square error (MSE) and highest PRE of the proposed estimator as compared to some existing estimator. We examine the proposed estimator with the highest PRE. With the use of Cramer's Rule, we can also determine the optimum value of the estimators. Numerical examples are used to validate theoretical observations. Then find out if the proposed estimator is more effective as compared to existing estimators. A proposed estimator verifies its practical applicability in real life widely used in various fields such as: Agriculture: To assess crop yields and disease prevalence. Economics: To study income levels and economic indicators and also use real life applications.