Advanced Classes of Regression-Type Estimators for the Ratio of Two Population Means in Stratified Sampling with Non-Response Adjustments

We have introduced two novel classes of regression-based estimators for estimating the ratio of two population means using an auxiliary variable in the context of stratified random sampling with non-response. We derived the expressions for the bias and mean square error (MSE) of these estimator classes and identified the minimum MSE values for the optimal constants. Specific members of these classes were identified, achieving minimum MSE for their respective optimal constants. Comparative analysis, utilizing both simulated and real world datasets, was conducted to assess the performance of the proposed estimators against conventional estimators. The results obtained from simulated and real data sets indicated that the proposed estimators exhibited lower approximate mean square error (AMSE)/mean square error (MSE) as the non-response rate increased and the sub-sampling fraction decreased, outperforming the traditional estimators.