فهرست مطالب n. h. al-noor

In the presence of outliers in the data set, the utilization of robust regression tools for mean estimationis a widely established practice in survey sampling with single auxiliary variable. Abid et al. (2018),with the aid of some non-conventional location measures and traditional OLS, proposed a class of meanestimators using information on two supplementary variates under a simple random sampling framework. The utilization of non-traditional measures of location, especially in the presence of outliers,performed better than existing conventional estimators. In this study, we have proposed a new class ofestimators of mean utilizing quantile regression. The general forms of MSE and MMSE are also derived.The theoretical findings are being reinforced by different real-life data sets and simulation study.

The presence of extreme events gives rise to outrageous results regarding population parametersand their estimates using traditional moments. Traditional moments are usually influenced by extremeobservations. 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.