Efficient Exponential Ratio Estimator for Estimating the Population Mean in Simple Random Sampling

A New Exponential Type Estimator for the Population Mean in Simple Random Sampling

‎In this paper‎, ‎a new estimate of exponential type of auxiliary information to help simple random sampling without replacement of the finite population mean is introduced‎. ‎This new estimator with a few other estimates using two real data sets are compared with the mean square error‎.

Efficient Exponential Ratio Estimator for Estimating the Population Mean in Simple Random Sampling

This paper proposes, with justification, two exponential ratio estimators of population mean in simple random sampling without replacement. Their biases and mean squared error are derived and compared with existing related ratio estimators. Analytical and numerical results show that at optimal conditions, the proposed ratio estimators are always more efficient than the regression estimator and ...

An Efficient Class of Exponential Chain Ratio Type Estimator for Finite Population Mean in Double Sampling

This paper presents a class of exponential chain ratio type estimator in double sampling for estimating finite population mean of the study variable, when the information on another additional auxiliary variable is known along with the main auxiliary variable. The property of proposed class of estimator has been studied. Comparison has been made with other competitive estimators. The proposed e...

Improvement in Estimating The Population Mean Using Dual To Ratio-Cum-Product Estimator in Simple Random Sampling

Improvement in estimating the population mean in simple random sampling

This paper proposes some estimators for the population mean using the ratio estimators presented in [C. Kadilar, H. Cingi, Ratio estimators in simple random sampling, Applied Mathematics and Computation 151 (2004) 893–902] and shows that all proposed estimators are always more efficient than the ratio estimators. This result is also supported by a numerical example. © 2005 Elsevier Ltd. All rig...