Estimation of Heterogeneous Population Variance using Memory-type Estimators based on EWMA statistic in the presence of Measurement Error for Time-Scaled Surveys

Abstract

Abstract In this present article, we have suggested memory-type ratio, exponential ratio, product and exponential product estimators based on exponentially weighted moving average statistic for the estimation of heterogeneous population variance using stratified sampling design in presence of measurement error for time-scaled surveys. Mathematical expressions of approximate mean square error are derived using Taylor and exponential expansions for the proposed memory-type estimators. We have also discussed the situations in which the memory-type estimators would perform efficiently than the conventional estimators. The results of extensive simulation study revealed that the proposed memory-type estimators based exponentially weighted moving average statistic would perform better than the conventional estimators in the presence measurement error for time-scaled surveys under certain condition.