A Generalized Exponential-Type Estimator for Population Mean Using Auxiliary Attributes

Abstract

In this dissertation, a generalized class of exponential type estimators for estimating the nite population mean by using the two auxiliary attributes and extended the proposed estimator in strati ed random sampling. Under simple random sampling without replacement (SRSWOR) scheme, the ex- pressions for bias and mean squared error of the proposed estimator up to rst order approximation are derived. The gain in relative e ciency of the proposed estimator has been found as compared to the usual mean estima- tors. An empirical study has been conducted to evaluate the performance of the proposed estimator by using eight populations. And we conclude the results of all classes from each population. Also we have derived conditions for superiority of the proposed estimator over the usual mean estimators and veri ed the condition numerically.