An enhanced auxiliary information-based estimator for finite population mean: Practical evidence from finance, transitional environments design, and radiation domains

The estimation of a finite population mean is a basic problem in survey sampling especially when the auxiliary variables have information that is closely related to the phenomenon being studied. In this study, we propose improved estimator under simple random sampling without replacement. The proposed estimator used dual auxiliary information to enhance estimation accuracy. The statistical characteristics of the suggested estimator are obtained up to the first order approximation. The empirical applications based on real data sets from transitional environmental, finance and radiation domains are used. The proposed estimator is compared to the existing estimators using real data sets and a simulation study. The proposed estimator is more precise by providing lower MSE and higher PRE compared to considered existing estimators. The results show that informative dual auxiliary information can be efficiently used for improved finite population mean estimation and indicate a practical estimation procedure which is flexible in terms of accommodating the various applications where informative variables are available, such as environmental transitions, radiation measurements, and others.