Generalized class of estimators for population distribution function in the presence of non-response

The nature of non-response in surveys is a very big challenge and the results obtained are often biased and may misguide research and policy conclusions. To resolve this we suggest flexible and generalized estimator that should be in place to correct the non-response and improve the precision of the estimations of population distribution function (DF). Their main contribution is associated with the creation of a purification process which is useful to rectify productivity-related distributions distortions brought about non-response. Our valid sample of the suggested approach is based on real survey data, which shows significant increases in reliability and representativeness of estimates of the productivity DF. A first-order approximation will help to evaluate recommended and existing estimators regarding their bias and MSE characteristics. The proposed estimators receive a numerical evaluation against current operational estimators. Multiple Specific estimators were developed to enlarge the generalized class of estimators. This research uses authentic data to check the precision of the estimators. The proposed estimators provide superior performance for population DF under non-response compared to initial estimators as indicated by our data. The estimation method reveals substantial efficiency superiority of the proposed MSE-based estimators. These findings highlight the practical applications of our method in real-life situations especially by the researchers and policymakers that require good data on economic and social analysis. In general, this works closing one of the major gaps in statistical methodology that exists by providing a powerful solution to the problem of population non-response.