Memory Type Estimator Of Population Mean Using Exponentially Weighted Moving Averages In Two-Phase Sampling

Abstract

To propose a new memory type estimator of Population Mean in two phase simple random sampling with one auxiliary using EWMA for time scaled surveys and to compare the proposed estimator with related previous estimator. Using Two-Phase 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. Simulation study has also been done to support the results by generating data. Exponentially weighted moving average (EWMA) statistic is a memory type statistic that used present and past information to estimate the population parameter. This study utilizes EWMA statistic to propose a ratio and product estimator for the surveys based on time scale. The usual ratio and product estimators consist of only current sample information, whereas the proposed estimators contains of current as well as past sample information. The mean square error expressions of the proposed estimators are derived and mathematical conditions are established to prove the efficiency of proposed estimators. It is revealed from the results of simulation study that utilization of the past samples information excels the performance of estimator in terms of efficiency.