Advancements in Population Mean Estimation for Circular Systematic Sampling

Abstract

Conventional measures of auxiliary variable(s) often yield unreliable results in the presence of outliers or extreme values. This article proposes a generalized estimator for the population mean by incorporating non-conventional measures under Circular Systematic Sampling (CSS). Expressions for the bias and Mean Squared Error (MSE) of the proposed estimators are derived, and the conditions under which they achieve minimum MSE are identified. Theoretical findings demonstrate the superiority of the proposed estimators over traditional unbiased, ratio, product, and regression estimators. Both theoretical and empirical studies indicate that the newly proposed estimators are more efficient than competing ones. The findings of this research will help researchers obtain more precise estimates of the population mean in the presence of outliers under CSS.