Development of a novel estimator using auxiliary information under non-response: Application to radiation data sets

Numerous disciplines rely on reliable population mean estimations under non-response conditions; this includes healthcare, economics, and weather forecasting, among others. Most research in sampling theory has focused on strategies to improve population mean estimate. In non-response scenarios, we still need a more precise estimate for population mean. In this study we develop an improved estimator employs an auxiliary variable within the framework of non-response using simple random sampling. Efficiency requirements are determined by comparing existing estimators with proposed estimator and looking at their mean squared errors and percentage relative efficiency. The empirical investigation makes use of radiation data sets and simulation investigation. The mean square errors and percentage relative efficiency of these estimators are examined through radiation data sets and simulation studies. The proposed estimators are significantly better than the existing ones, as shown by the numerical results. From the numerical results we see that our suggested estimator provide minimum mean square error and higher percentage relative efficiency. The significance and potential applications of our proposed estimator are highlighted by these outcomes.