Modified Estimator of Finite Population Variance under Stratified Random Sampling

In this paper, a generalized estimator of finite population variance using the auxiliary information under stratified random sampling is proposed. The expressions for bias and mean square error equations of the proposed estimator are derived up to the first degree of approximation. The theoretical efficiency conditions under which the proposed estimator is better than some existing estimators are obtained. The performances of the existing and proposed estimators were assessed using three real datasets based on the criteria of minimum mean square error and supreme percentage relative efficiency. Evidence from the study showed that the proposed estimator performed better and is more efficient than some existing estimators considered.