Flexible Robust Regression-Ratio Type Estimators and Its Applications

In real-world situations, the data set under examination may contain uncommon noisy measurements that unreasonably affect the data’s outcome and produce incorrect model estimates. Practitioners employed robust-type estimators to reduce the weight of the noisy measurements in a data set in such a scenario. Using auxiliary information that will produce reliable estimates, we have looked at a few flexible robust-type estimators in this study. In order to estimate the population mean, this study presents unique flexible robust regression type ratio estimators that take into account the data from the midrange and interdecile range of the auxiliary variables. Up to the first order of approximate computation, the bias and mean square were calculated. In order to compare the flexibility of the proposed estimator to those of the existing estimators, theoretical conditions were also obtained. We took into account data sets containing outliers for empirical computation, and it was found that the suggested estimators produce results with higher precision than the existing estimators.