A Generalized Class of Exponential-type Estimators for Estimating the Population Mean in Post Stratified Sampling

Abstract

This paper addresses the problem of estimating the population mean using auxiliary information under post stratified sampling. We have suggested a generalized class of estimators for population mean in post stratified sampling. Expressions of bias and mean squared error of suggested class of estimators up to the first order of approximation have been obtained. We have obtained the optimum values of scalars under which the class of estimators attained its minimum mean squared error. Efficiency conditions are obtained under which the suggested class of estimators is better than the usual unbiased estimator, combined ratio estimator and difference estimator. We have also studied the properties of some members of the proposed class of estimators. We have further compared the minimum mean squared error of the suggested class of estimators with that of the members. An empirical study is carried out through a natural population data. Empirical study gives insight on the magnitude of the efficiency of the members of the developed class of estimators.