Abstract
In sample surveys, collection of auxiliary information together with the main variable of interest is very important to increase the efficiency of the estimators of population parameters of interest. Regression and ratio estimation are very popular and are widely used methods that benefit from the use of auxiliary information for the estimation of population parameters like mean, total, variance, proportion etc. A generalized semi-exponential type estimator is proposed in this paper using two auxiliary variables under the framework of systematic sampling. The expressions of approximate bias and mean square error of the proposed estimator have been derived. Algebraic conditions have been obtained under which the proposed estimator is more efficient than the competing estimators considered here. An empirical study has been carried out to show the improvement in efficiency of the proposed estimator as compared to the existing estimators.
Highlights
IntroductionNo single estimation procedure will always work the best. Various sampling designs are available for different populations under different situations
In survey sampling, no single estimation procedure will always work the best
Systematic sampling is often advantageous over the simple random sampling for being easy to implement and often providing increased precision in estimates of population parameters of interest
Summary
No single estimation procedure will always work the best. Various sampling designs are available for different populations under different situations. Systematic sampling is often advantageous over the simple random sampling for being easy to implement and often providing increased precision in estimates of population parameters of interest Under this design, only first sampling unit is selected randomly and the subsequent units are selected by according to certain rules. ”Cochran (1946) declared that apart from easy to implement, systematic sampling often provides more efficient estimators as compared to the simple random sampling (SRS) or stratified random sampling for different types of populations under different situations. For more recent work on systematic sampling including some exponential type estimators using auxiliary information, one can refer to Singh et al (2011), Singh and Solanki (2012), Singh and.
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