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2019•Statistics in Transition New SeriesOpen access

A NEW RATIO ESTIMATOR: AN ALTERNATIVE TO REGRESSION ESTIMATOR IN SURVEY SAMPLING USING AUXILIARY INFORMATION

Mir Subzar, Showkat Maqbool, T. A. Raja, Prayas Sharma

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Abstract

Abstract The most dominant problem in the survey sampling is to obtain the better ratio estimators for the estimation of population mean or population variance. Estimation theory is enhanced by using the auxiliary information in order to improve on designs, precision and efficiency of estimators. A modified class of ratio estimator is suggested in this paper to estimate the population mean. Expressions for the bias and the mean square error of the proposed estimators are obtained. Both analytical and numerical comparison has shown the suggested estimator to be more efficient than some existing ones. The bias of the suggested estimator is also found to be negligible for the population under consideration, indicating that the estimator is as good the regression estimator and better than the other estimators under consideration.

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What this paper is about

Abstract The most dominant problem in the survey sampling is to obtain the better ratio estimators for the estimation of population mean or population variance. Estimation theory is enhanced by using the auxiliary information in order to improve on designs, precision and efficiency of estimators. A modified class of ratio estimator is suggested in this paper to estimate the population mean. Expressions for the bias and the mean square error of the proposed estimators are obtained. Both analytical and numerical comparison has shown the suggested estimator to be more efficient than some existing ones. The bias of the suggested estimator is also found to be negligible for the population under consideration, indicating that the estimator is as good the regression estimator and better than the other estimators under consideration.

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Available abstract

Abstract The most dominant problem in the survey sampling is to obtain the better ratio estimators for the estimation of population mean or population variance. Estimation theory is enhanced by using the auxiliary information in order to improve on designs, precision and efficiency of estimators. A modified class of ratio estimator is suggested in this paper to estimate the population mean. Expressions for the bias and the mean square error of the proposed estimators are obtained. Both analytical and numerical comparison has shown the suggested estimator to be more efficient than some existing ones. The bias of the suggested estimator is also found to be negligible for the population under consideration, indicating that the estimator is as good the regression estimator and better than the other estimators under consideration.

Key concepts: Estimator, Minimum-variance unbiased estimator, Ratio estimator, Mean squared error, Efficient estimator, Statistics, Bias of an estimator, Mathematics

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