Robust ratio-type estimators in simple random sampling

In sampling theory, ratio-type estimators are extensively used to estimate the population mean when the correlation between study and auxiliary variables is positively high. In this study, we incorporate robust modified maximum likelihood estimators (MMLEs) into Kadilar-Cingi estimators and provide their properties theoretically. We support the theoretical results with simulations under numerous super-population models, and study the robustness properties of these modified estimators. We show that utilization of MMLEs in estimating the mean of a finite population leads to robust estimates, which is very advantageous when we have non-normality or other common data anomalies such as outliers.