New memory-based ratio estimator in survey sampling


  • Irfan Aslam Department of Statistics, Government Islamia Graduate College, Lahore, Punjab, Pakistan.
  • Muhammad Noorul Amin Department of Statistics, COMSATS University Islamabad, Lahore Campus, Lahore, Punjab, Pakistan.
  • Amjad Mahmood Punjab College of Information Technology, Lahore, Punjab, Pakistan | Hailey College of Commerce, University of the Punjab, Lahore, Punjab, Pakistan.
  • Prayas Sharma Department of Statistics, Babasaheb Bhimrao Ambedkar University, Lucknow, India.



Auxiliary information, Ratio estimator, Smoothing constant, Mean square, Relative efficiency, Simple random sampling, Memory type estimate, New estimator, Simulation


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 ?, as the cost of the smoothing constant ? decreases from one towards zero, and we gain the efficiency of the suggested estimator. For ? = 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.


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How to Cite

Aslam, I., Amin, M. N., Mahmood, A., & Sharma, P. (2024). New memory-based ratio estimator in survey sampling. Natural and Applied Sciences International Journal (NASIJ), 5(1), 168–181.



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