Improved estimation strategy of mean using linear cost function under stratified sampling

The population parameters estimation for several areas involves the development of various methods and techniques to improve the accuracy and efficiency of estimating population parameters. In order to accommodate for the impacts of technology, such as big data analytics, and on the overall cost of sampling, research must modify cost models. Thus there is still gap for searching more efficient estimators. In this study, the authors propose a ratio-product-cum-exponential-cum-logarithmic type estimator for the estimation of population mean by implying one auxiliary variable in stratified random sampling using conventional product, exponential, and logarithmic type estimators. We find the bias and MSE of proposed estimator in first order approximation and compared minimum MSE and highest PRE of proposed estimator with some existing estimators. The population parameter estimation in cost functions, potentially leading to more accurate and cost-effective survey designs. Now using the linear cost function and compute the minimum MSE and highest PRE of proposed estimator and compare the same existing estimators. The proposed estimator is more effective than other existing estimators, according to theoretical observations and numerical examples. Then proposed estimator in making them indispensable in fields ranging from public policy.