Enhancing the efficiency of the exponential estimator for population mean estimation in the presence of non-response by utilizing auxiliary information

In survey research, researchers often face the issue of non-response or missing data, which significantly reduces the accuracy and efficiency of estimators used to estimate parameters of interest. To address these problems, researchers typically use auxiliary information to enhance the accuracy and efficiency of the estimators. Therefore, this study focuses on improving the efficiency of estimators for population mean estimation under non-response by utilizing auxiliary information to create an exponential estimator that is more accurate and efficient than other estimators studied. The researcher examines some important properties of the newly developed estimator, such as Mean Squared Error (MSE) and Minimum Mean Squared Error (MMSE), and compares the performance of all related estimators regarding theory, application to real data, and simulation studies. The findings indicate that the newly developed estimator provides better accuracy and efficiency than the others under the same circumstances.