Theoretical Modeling by Addressing Nonresponse Complications to Improve the Population Mean Under Stratified Random Sampling: Application and Analysis

An important part of survey sampling is additional information, which allows for more precise estimates of population parameters such as population distribution function, mean, variance, and median. The best outcomes can be assured in this manner. Researchers using survey sampling face the risk of missing important details while attempting to compile an entirely comprehensive overview. Therefore, the computed value could be affected by nonresponse processes which occur simultaneously. Because of this problem, we offer new estimators that use a stratified random sample based on the nonresponse method to utilize auxiliary information and the rank of that auxiliary information. It has been demonstrated that the suggested estimators are efficient at predicting the process using metrics such as mean square error and percentage relative efficiency. As compared to the existing estimators, our suggested estimators work more efficiently and have a reduced minimum mean square error. Therefore, it is indicated that the recommended estimators perform sufficiently. Our proposed estimator is the most effective estimator, according to the numerical study in comparison to the adopted existing estimators. The results of this investigation will be useful for improving survey sampling methods in the future.