Efficient Classes of Estimators of Population Mean under Various Allocation Schemes in Stratified Random Sampling

The present paper is an extension of the work published in Kumar and Vishwakarma (Proceedings of the National Academy of Sciences, India, Section A: Physical Sciences, 90(5): 933-939, 2020). In this paper, various sample allocation schemes are utilized to derive the mathematical expressions for mean square errors (MSEs) of several well-known estimators of population mean in stratified random sampling. Moreover, the effects of various allocation schemes on the estimation of mean, are demonstrated theoretically as well as empirically. The findings of the study reveal that the Neyman allocation provides a smaller variance (or MSE, as the case may be) as compared to that of Equal and Proportional allocation schemes for the concerned estimators. Moreover, the proposed classes of estimators are dominant over the pre-existing estimators under the various allocation schemes considered in the study.