Some Memory Type Estimators for Population Variance in Survey Sampling

Abstract

Reducing and estimating population variation is important in survey sampling. These variations can occur in any sampling design, including stratified random sampling. In stratified random sampling the difference in stratum weights increase the variation. This problem can be control with the use of calibration techniques, and auxiliary information. By using this technique, we can increase the accuracy and efficiency of estimator. Improving the efficiency of estimator for population variance estimation is the primary goal of this study. Both exponentially weighted moving average (EWMA) and extended exponentially weighted moving average (EEWMA) memory type statistics are used in this study to estimate population variance. EWMA use only current data whereas EEWMA use both current and past observations. The EWMA and EEWMA statistics are used to build the calibration variance and ratio estimators. The mean square errors are calculated, and the effectiveness of the estimators is illustrated through a discussion of mathematical comparisons. To assess the efficiency of the proposed memory type estimators, a simulation study is conducted and MSE are compare with existing estimators. The results show that proposed estimators are perform efficiently as compared to the existing estimators