An improved regression type estimator of population mean with two auxiliary variables and its variant using robust regression method

Abstract

In this paper, we have proposed an improved estimator of the population mean in simple random sampling without replacement by applying Searls (1964) technique on the estimator given by Roy (2003). We have also proposed its extension to stratified random sampling which is a generalization of an estimator given by Grover (2006). We have also used robust regression method to these proposed traditional estimators and developed new robust estimators in simple random sampling as well as in stratified random sampling. The biases and mean square errors of these proposed traditional estimators have been obtained, up to first order of approximation. After comparison of mean square errors of various traditional estimators, it has been found that the proposed traditional estimator is more efficient than its competitor estimators considered in this paper. The proposed estimator based upon robust regression method is more efficient than proposed traditional estimators in simple random sampling and in stratified random sampling under some conditions. The theoretical results so obtained have been verified with the help of some empirical examples and simulation studies.