A Novel Robust Class of Estimators for Estimation of Finite Population Mean: A Simulation Study

Abstract

In the existing survey sampling literature, the ratio-type estimators are an obvious choice to estimate the finite population mean when auxiliary information related to the study variable is readily available. Typically, auxiliary information is incorporated into ratio-type estimators by using conventional measures such as mean, range, coefficient of kurtosis, coefficient of skewness and coefficient of correlation, etc. which are less efficient when extreme observation are present in the data. This study provides a remedy and enhances the efficiency of the ratio-type estimators of population mean in the presence of extreme observations by proposing dual auxiliary variables based exponential-cum-ratio class of estimators which integrates both conventional and non-conventional measures under simple random sampling without replacement. The expression of the mean squared error and theoretical efficiency conditions for proposed class of estimators have been obtained for comparison purposes. A simulation study was carried out based on contaminated normal distribution and the robustness of the proposed estimators has been assessed in the presence of extreme observations. For practical implementation, six real data sets have been used to compare the performance of the proposed estimators with competing estimators to support the theoretical results. The theoretical and empirical results suggest that the proposed estimators are more precise than usual mean as well as existing estimators’ ratio-type considered in this study.