Variance estimation has been a major concern in sample survey theory. The problem
in estimation theory is to determine estimators that have smaller variance under a
given model speci cation. However, existing variance estimators su er from boundary
problems and outlier sensitivity. To address this, a robust variance estimate of
the ratio estimator of the population mean using a multiplicative bias correction technique
under model based approach is considered. Asymptotic properties of the robust
variance estimator are investigated. Also a comparative study of the existing variance
estimators and the derived robust variance estimator of the population mean is studied.
The results of the study show that under mild assumption, the derived variance
estimator of the population mean is asymptotically more consistent and has a better
coverage probability as compared to rival variance estimators of the population mean.