L-Moments and calibration-based variance estimators under double stratified random sampling scheme: Application of Covid-19 pandemic

Document Type : Article

Authors

1 - Department of Mathematics and Statistics, International Islamic University, Islamabad 46000, Pakistan - Department of Mathematics and Statistics - PMAS-Arid Agriculture University, Rawalpindi 46300, Pakistan

2 Department of Mathematics and Statistics, International Islamic University, Islamabad 46000, Pakistan

3 - Department of Mathematics, College of Science, King Khalid University, Abha 62529, Saudi Arabia - Statistical Research and Studies Support Unit, King Khalid University, Abha 62529, Saudi Arabia

4 Department of Mathematics and Statistics - PMAS-Arid Agriculture University, Rawalpindi 46300, Pakistan

5 Department of Mathematics, College of Science, Mustansiriyah University, Baghdad 10011, Iraq

Abstract

The presence of extreme events gives rise to outrageous results regarding population parameters
and their estimates using traditional moments. Traditional moments are usually influenced by extreme
observations. In this paper, we propose some new calibration estimators under L-Moments scheme for variance which is one of the most important population parameters. Some suitable calibration constraints under double stratified random sampling are also defined for these estimators. Our proposed estimators based on L-Moments are relatively more robust in presence of extreme values. The empirical efficiency of proposed estimators is also calculated through simulation. Covid-19 pandemic data from January 22, 2020, to August 23, 2020, is considered for simulation study.

Keywords

References

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Scientia Iranica
Volume 30, Issue 2
Transactions on Industrial Engineering (E)
March and April 2023
Pages 814-821

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