Ranked Set Sampling Based Regression Estimators in Two-Stage Sampling Design

This paper examines the use of ranked set sampling in the second stage of the two-stage sampling design to improve the regression estimator. The mean square errors of the suggested estimators are computed, and both theoretical and simulation results demonstrate improved estimates as compared to the conventional sampling methods, particularly in situations with limited resources or costly measurements. This approach provides a promising method for enhancing regression analysis in complex sampling scenarios.