Improved Estimation of Population Mean Through Known Conventional and Non-Conventional Measures of Auxiliary Variable

This paper proposes a generalized class of difference-cum-exponential-type estimators for population mean under simple random sampling without replacement through known conventional and non-conventional auxiliary information. It is observed that some well-known estimators are the members of our proposed class. Moreover, proposed class of estimators behaves efficiently than competing estimators under some simple conditions. Theoretical findings are confirmed with numerical illustration by using six real-life datasets. In addition, Monte Carlo simulation study on four real populations also approved the potential of the proposed class against competing estimators.