Regression-Cum-Ratio Mean Imputation Class of Estimators using Non-Conventional Robust Measures

Abstract

Different imputation strategies have been developed by several authors to
take care of missing observations during analyses. Nevertheless, the
estimators involved in some of these schemes depend on known
parameters of the auxiliary variable which outliers can easily influence. In
this study, a new class of ratio-type imputation methods that utilize
parameters that are free from outliers has been presented. The estimators
of the schemes were obtained and their MSEs were derived up to first-order
approximation using the Taylor series approach. Also, conditions for which
the new estimators are more efficient than others considered in the study
were also established. Numerical examples were conducted and the results
revealed that the proposed class of estimators is more efficient.