Application of Log-Type Estimators for Addressing Non-Response in Survey Sampling Using Real Datasets

There is a difficulty in survey sampling when non-response (NR) occurs in the process of estimating the population parameters. This study examines the effectiveness of combined and separate log-type estimators when using bivariate auxiliary information when NR occurs in data. In this study, we propose families of novel log-type estimators under various scenarios. We performed an analysis on the reliability and efficiency of our proposed estimators in situations when NR occurs in both study and auxiliary variables and when NR occurs only in study variables. In this study, we have concentrated on certain issues like how the non-response effects the estimators’ efficiency, how different NR rates effect the precision of estimators, and how the combined and separate types of estimators handle the problem of NR. We proved the efficiency of our proposed estimators by using the bias and mean square error (MSE) metrics under different NR rates, illustrating the positive correlation between higher NR rates and increased errors. To evaluate the impact of NR on MSE values, we took four real datasets, which included a cost of living index dataset for 121 nations and another dataset which is essential for forecasting solar UV radiation hazards influenced by environmental factors, thus enhancing public health awareness and preventive strategies. Additionally, a simulation study comprising 10,000 iterations was also performed. This study provides survey practitioners with valuable guidance on selecting strong estimation methods to enhance the accuracy and efficiency of survey estimates in the context of non-response. This investigation contributes to the domain of survey sampling by demonstrating the robustness and effectiveness of log-type estimators. These estimators enhance survey findings by effectively addressing NR issues.