Ratio-Cum-Product Estimator Using Multiple Auxiliary Attributes in Single Phase Sampling

A Ratio-cum-Dual to Ratio Estimator of Population Variance Using Qualitative Auxiliary Information Under Simple Random Sampling

In this paper we have proposed a class of ratio-cum-dual to ratio estimators for estimating population variance of the variable under study, using known values of some population parameters of auxiliary variable, which is available in the form of an attribute. The expressions for the bias and mean squared error of the proposed estimators have been derived upto the first order of approximation. ...

An Alternative Ratio-Cum-Product Estimator of Finite Population Mean Using Coefficient of Kurtosis of Two Auxiliary Variates in Two-Phase Sampling

This paper deals with the problem of estimation of population mean in two-phase sampling. A ratio-product estimator of population mean using known coefficient of kurtosis of two auxiliary variates has been proposed. In fact, it is a two-phase sampling version of Tailor et al. (2010) estimator and its properties are studied. Proposed estimator has been compared with usual unbiased estimator, cla...

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

A Generalized Difference-cum-Ratio Type Estimator for the Population Mean in Double Sampling

Using double sampling technique, a generalized difference-cum-ratio type estimator has been proposed for estimating the population mean. The expressions for bias and mean square error of proposed estimator have been obtained. The conditions under which proposed estimator is better than the regression estimator and mean per unit estimator have also been obtained. The gain in efficiency over the ...