Difference-type-exponential estimators based on dual auxiliary information under simple random sampling

Abstract

Auxiliary information plays a vital role in parameter selection and estimation to achieve efficient estimates of unknown population parameters. Dual use of auxiliary information, i.e., \original"and \ranked"auxiliary variables, helps increase the efficiency of estimators. In this paper, the performance of difference-type-exponential estimators was proposed and evaluated based on dual auxiliary information for population mean under simple random sampling. Mathematical expressions for the bias and the mean squared error of the proposed estimators were obtained. Three real-life data sets and Monte Carlo simulation studies were carried out for illustration. The results of empirical and simulation studies indicate that the proposed estimators outperformed their counterparts in terms of mean square errors and percentage relative efficiency.