DEVELOPMENT OF A MEMORY-BASED RATIO ESTIMATOR IN FINITE POPULATION SAMPLING

This study proposes a new estimator based on the Exponential Weighted Moving Average (EWMA) statistic by following the concept of Noor-ul-Amin (2021). The EWMA model consumed contemporary and empirical data to enhance the competence of the population mean estimation. The memory type estimate is proposed with a twofold utilisation of Auxiliary Information (AUI) in alignment with the sampling type, i.e., Simple Random Sampling (SRS). A detailed numerical study and analysis are conducted to estimate the projected efficiency. This study provides an efficient estimator for population mean in the occurrence of time series data. The extra advantage of using EWMA statistics instead of classical statistics is that we can get better effectiveness of the mean estimate of the population under SRS by altering the significance of the smooth constant λ, as the cost of the smoothing constant λ decreases from one towards zero, and we gain the efficiency of the suggested estimator. For λ = 1, the proposed estimator performs the same as their comparative estimator. This expands incompetence over the presented ones statistically demonstrated in mean square error and relative effectiveness. Previous research is also accessed with everyday life information that indicates the conclusions of the simulation investigation