Improved Estimators of Population Mean Using Two Auxiliary Variables in Stratified random Sampling

An Improved Suggestion in Stratified Random Sampling Using Two Auxiliary Variables

Estimator of a Population Mean Using Two Auxiliary Variables in Simple Random Sampling

We propose a new ratio estimator using two auxiliary variables in simple random sampling. We obtain mean square error (MSE) equation of this estimator and theoretically show that our proposed estimator is more efficient than the traditional multivariate ratio estimator under a defined condition. In addition, we support this theoretical result with the aid of a numerical example.

Dual to Ratio cum Product Estimators of Finite Population Mean Using Auxiliary Attribute(s) in Stratified Random Sampling

The problem of estimating the population mean in un-stratified sampling strategies to estimate the finite the presence of an auxiliary variable has been widely discussed in finite population sampling literature [1], suggested a class of estimators of the population mean using one auxiliary variable in the stratified random sampling and examined the MSE of the estimators up to the kth order of a...

Improvement in Estimating Population Mean using Two Auxiliary Variables in Two-Phase Sampling

This study proposes improved chain-ratio type estimator for estimating population mean using some known values of population parameter(s) of the second auxiliary character. The proposed estimators have been compared with two-phase ratio estimator and some other chain ratio type estimators. The performances of the proposed estimators have been supported with a numerical illustration.

A Class of Ratio Estimators of a Finite Population Mean Using Two Auxiliary Variables

In sample surveys, it is usual to increase the efficiency of the estimators by the use of the auxiliary information. We propose a class of ratio estimators of a finite population mean using two auxiliary variables and obtain mean square error (MSE) equations for the class of proposed estimators. We find theoretical conditions that make proposed family estimators more efficient than the traditio...