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

Extreme events gives rise to outrageous results in terms of population-related parameters and their estimates are usually done using traditional moments. Traditional moments are usually affected by extreme observations. This study aims to propose some new calibration estimators considering the L-Mom...

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Bibliographic Details
Published inScientia Iranica. Transaction E, Industrial engineering Vol. 30; no. 2; pp. 814 - 821
Main Authors Shahzad, U, Ahmad, I, Almanjahie, I Mufrah, Hanif, M, H Al-Noor, N
Format Journal Article
LanguageEnglish
Published Tehran Sharif University of Technology 01.03.2023
Subjects
Calibration
COVID-19
Estimators
Extreme values
Leaves
Pandemics
Parameter estimation
Parameters
Random sampling
Sample variance
Simulation
Statistical sampling
Variance
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Summary:Extreme events gives rise to outrageous results in terms of population-related parameters and their estimates are usually done using traditional moments. Traditional moments are usually affected by extreme observations. This study aims to propose some new calibration estimators considering the L-Moments scheme for variance, which is one of the most important population parameters, a number of suitable calibration constraints under double stratified random sampling were defined for these estimators. The proposed estimators, which were based on L-Moments, were relatively more robust despite extreme values. The empirical efficiency of the proposed estimators was also assessed through simulation. Covid-19 pandemic data from January 22, 2020 to August 23, 2020 was taken into account in the simulation study.
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DOI:10.24200/sci.2021.56853.4942