An Estimator of the Mean Estimation of Study Variable Using Median of Auxiliary Variable

Improved Estimation of Population Mean Using Median and Coefficient of Variation of Auxiliary Variable

This manuscript deals with the estimation of population mean of the variable under study using an improved ratio type estimator utilizing the known values of median and coefficient of variation of auxiliary variable. The expressions for the bias and mean square error (MSE) of the proposed estimator are obtained up to the first order of approximation. The optimum estimator is also obtained for t...

Improved ratio type estimators of population mean based on median of a study variable and an auxiliary variable

This paper deals with efficient ratio type estimators for estimating finite population mean under simple random sampling scheme by using the knowledge of known median of a study and an auxiliary variable. Expressions for the bias and mean squared error of the proposed ratio type estimators are derived up to first order of approximation. It is found that our proposed estimators perform better as...

An Improved Estimation Procedure of the Mean of a Sensitive Variable Using Auxiliary Information

Let Y be the variable under study, a sensitive variable which can’t be observed directly. Let X is a non – sensitive auxiliary variable which is strongly correlated with Y. Let S be a scrambling variable independent of the study variable Y and the auxiliary variable X. The usual additive model used for gathering information on quantitative sensitive variable is due to Himmelfarb & Edgell [3]. T...

Improved Estimation of the Population Mean Using Known Parameters of an Auxiliary Variable

assessment of the efficiency of s.p.g.c refineries using network dea

data envelopment analysis (dea) is a powerful tool for measuring relative efficiency of organizational units referred to as decision making units (dmus). in most cases dmus have network structures with internal linking activities. traditional dea models, however, consider dmus as black boxes with no regard to their linking activities and therefore do not provide decision makers with the reasons...