A Robust Regression Type Estimator for Estimating Population Mean under Non-Normality in the Presence of Non-Response
In sampling theory, regression type estimators are extensively used to estimate the population mean when the correlation between study and auxiliary variables is high. In this study, we incorporate robust modified maximum likelihood estimators (MMLEs) into regression type estimator in the presence of non-response and their properties have been obtained theoretically. For the support of the theoretical outcomes, simulations under several super-population models have been made. We study the robustness properties of these modified estimators. We show that utilization of MMLEs in estimating finite populations mean leads to robust estimates, which is very advantageous when we have non-normality or other common data anomalies such as outliers.
