A Modified Ratio-Cum-Product Estimator of Finite Population Mean in Stratified Random Sampling

Modified Ratio cum Product Estimator for Estimation of Finite Population Mean with Known Correlation Coefficient

In sampling theory, a wide variety of techniques is used to obtain efficient estimators for the population mean. The commonly used method to obtain the estimator for population mean is simple random sampling without replacement (SRSWOR) when there is no auxiliary variable available. There are methods that use the auxiliary information of the study characteristics. If there exists an auxiliary v...

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...

A New Difference-Cum-Exponential Type Estimator of Finite Population Mean in Simple Random Sampling

Auxiliary information is frequently used to improve the accuracy of the estimators when estimating the unknown population parameters. In this paper, we propose a new difference-cum-exponential type estimator for the finite population mean using auxiliary information in simple random sampling. The expressions for the bias and mean squared error of the proposed estimator are obtained under first ...

Improvement in Estimating The Population Mean Using Dual To Ratio-Cum-Product Estimator 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.