A Novel Class of Ratio Estimators Incorporating Auxiliary Variables in Simple Random Sampling for Estimating Population Mean: An Application to Real-Life Data

This paper presents a novel class of ratio estimators using non-conventional measures as auxiliary information to estimate the population mean of the study variable under simple random sampling without replacement. Three special cases are discussed, utilizing the coefficient of variation, median, and quartile deviation as auxiliary variables. The bias and mean square error (MSE) of the proposed estimators are derived up to the first order of approximation. Furthermore, theoretical conditions are established to compare the proposed class with existing estimators, supported by real-life data. Numerical results demonstrate that the proposed class of estimators is more efficient than traditional and other ratio-type estimators.