AN EFFICIENT COMPROMISED IMPUTATION METHOD FOR ESTIMATING POPULATION MEAN

This paper suggests a modified new ratio-product-exponential imputation procedure to deal with missing data in order to estimate a finite population mean in a simple random sample without replacement. The bias and mean squared error of our proposed estimator are obtained to the first degree of approximation. We derive conditions for the parameters under which the proposed estimator has smaller mean squared error than the sample mean, ratio, and product estimators. We carry out an empirical study which shows that the proposed estimator outperforms the traditional estimators using real data.