Families of Estimators for Estimating Mean Using Information of Auxiliary Variate Under Response and Non-Response

This research article is concerned with the efficiency improvement of estimators for finite population mean under complete and incomplete information rising as a result of non-response. Different families of estimators for estimating the mean of study variate via known population mean, proportion and rank of auxiliary variate under different situations are proposed along with their bias and mean square error (MSE). Optimum conditions are suggested to attain minimum mean square error of proposed families of estimators. Further the problem is extended for the situation of unknown parameters of auxiliary variate and two phase sampling families of estimators are suggested along with their properties under fixed cost and precision. Employing real data sets, theoretical and empirical comparisons are executed to explain the efficiency of the proposed families of estimators.

A family of estimators of population mean using multi-auxiliary variate and post-stratification

This paper suggests a family of estimators of population mean using multiauxiliary variate based on post-stratified sampling and its properties are studied under large sample approximation. Asymptotically optimum estimator in the class is identified alongwith its approximate variance formulae. The proposed class of estimators is also compared with corresponding unstratified class of estimators based on estimated optimum value. At the end, an empirical study has been carried out to support the proposed methodology.