A family of ratio estimators for population mean in extreme ranked set sampling using two auxiliary variables

Abstract

In this paper we have adopted the Khoshnevisan et al. (2007) family of estimators to extreme ranked set sampling (ERSS) using information on single and two auxiliary variables. Expressions for mean square error (MSE) of proposed estimators are derived to first order of approximation. Monte Carlo simulations and real data sets have been used to illustrate the method. The results indicate that the estimators under ERSS are more efficient as compared to estimators based on simple random sampling (SRS), when the underlying populations are symmetric.