GENERALIZED FAMILY OF EXPONENTIAL TYPE ESTIMATORS FOR THE ESTIMATION OF POPULATION COEFFICIENT OF VARIATIONGENERALIZED FAMILY OF EXPONENTIAL TYPE ESTIMATORS FOR THE ESTIMATION OF POPULATION COEFFICIENT OF VARIATION

Abstract

Under simple random sampling, an improved family of estimators is proposed by incorporating auxiliary information to minimize the variation using the known coefficient of variation. The expression for bias and mean square error (MSE) of the generalized class are derived up to first order of approximation. The efficiency conditions of proposed family are also derived with the competitor estimators. The applications of estimator are discussed using simulation study and real-life data sets for the efficiency comparisons of proposed family with some existed estimators. In the light of the results of simulation study and real-life applications it is found that proposed family of estimators have lower mean square errors as compare to the existing once which shows that the proposed class of estimators is more precis. It is also concluded that when correlation between study and auxiliary variables increases, the proposed generalized family of estimators provides more efficient results.