A new auxiliary variables-based estimator for population distribution function under stratified random sampling and non-response

Population distribution function is a particular area in sample surveys, and several researchers have worked to improve the accuracy of this study by using the auxiliary data. Recent studies estimate the population distribution function by applying stratified random sampling and non-response techniques, but there are some limitations in using the auxiliary data. However, we improve this study, which aims to maximize the accuracy of estimating the population distribution function under the combined effect of stratified random sampling and non-response groups. To achieve this goal in the condition of both sampling techniques, we introduce the use of a study variable and two auxiliary variables (mean and ranks). We conduct various estimations for real-world populations for theoretical and numerical findings. The results obtained from these estimators consistently demonstrate the better performance of the proposed classes of estimators over the currently existing estimators. This work also finds a comprehensive simulation analysis to evaluate the performance of various estimators. These findings show that the effectiveness of the proposed estimator significantly improves estimation accuracy. For additional validation and understanding of the relative effectiveness of the proposed estimators, this study also provides comparative graphs showing their performance relative to other current estimators.