A two parameter variance estimator using auxiliary information

We propose a two parameter ratio-product-ratio estimator for estimating a finite population variance based on simple random sampling without replacement. The bias and mean square error of the proposed estimator are obtained to the first degree of approximation. We have derived the conditions for the parameters under which the proposed estimator has smaller mean square error than the sample variance, ratio and product estimators. We carry out an application showing that the proposed estimator outperforms the traditional estimators using different data sets.