A Nonconventional Auxiliary Information Based Robust Class of Exponential-type Difference Estimators

This study proposes a new class of improved exponential-type difference estimators of finite population mean by using supplementary information of known median along with suitable combinations of the conventional and non-conventional measures of the auxiliary variables under simple random sampling scheme. The expressions for the mean squared error and minimum mean squared error are derived up to first order of the approximation. Six real data sets were used to assess the performance of proposed class of estimators in comparison with existing estimators. The compariosn established that the suggested class of estimators are efficient than their existing counterparts considered in this study. To further support the findings of the numerical comparison, a simulation study was carried out which also proved the superiority of the proposed class of estimators of population mean. To gauge the performance of the propsoed class of estimators when some outliers are present in the data, a robustness study was carried out which showed that the proposed estimators considerably outperform their existing counterparts in terms of lower mean squared errors.