Exponential Type Ratio and Product Estimator of Finite Population Mean in Stratified Sampling

Objective: This article presents an exponential-type ratio and product estimator of the finite population mean within the stratification framework. A common technique in survey sampling is stratification, which divides the population into similar subsets to improve the estimator's accuracy. Methods: This paper tackles the problem of determining the expression for bias and Mean Square Error (MSE) for the population up to the 1st degree of the approximation when stratification is present. Findings: Here it is obtained the optimum value of the modified estimator. The modified estimator is more effective than the unbiased, ratio and product type estimators. We have used one data set through the stratified random sampling technique, and the relative efficiency of the estimators obtained from the population set is higher. Novelty: New proposed stratification estimators that improve the existing mixed ratio and product exponential types. The primary goal of this study is to investigate how well the suggested estimators perform compared to current estimators. Thorough simulation research to test the estimator's accuracy and efficiency. Both theoretical and practical research have shown that the proposed estimators outperform competing estimators. Keywords: Finite population mean, Stratification, Bias, Mean square error, Percent relative efficiency