In this paper, we suggest an estimator using two auxiliary variables in stratified random sampling. The propose estimator has an improvement over mean per unit estimator as well as some other considered estimators. Expressions for bias and MSE of the estimator are derived up to first degree of approximation. Moreover, these theoretical findings are supported by a numerical example with original data.Key words: Study variable, auxiliary variable, stratified random sampling, bias and mean squared error.
Auxiliary variable is commonly used in survey sampling to improve the precision of estimates. Whenever there is auxiliary information available, we want to utilize it in the method of estimation to obtain the most efficient estimator. In this paper using multiauxiliary information we have proposed estimators based on geometric and harmonic mean. It was also shown that estimators based on harmonic mean and geometric mean are less biased than Olkin (1958) and Singh (1967) estimators under certain conditions. However, the MSE of Olkin (1958) estimator and geometric and harmonic estimators are same up to the first order of approximations.
Abstract:This article presents the problem of estimating the population mean using auxiliary information in the presence of measurement errors. We have compared the three proposed estimators being the exponential ratio-type estimator, Solanki et al. (2012) estimator, and the mean per unit estimator in the presence of measurement errors. Financial Model by Gujrati and Sangeetha (2007) has been employed in our empirical analysis. In that, our investigation has indicated that our proposed general class of estimator t 4 is the most suitable estimator with a smaller MSE relative to other estimators under measurement errors.
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