On Quantiles Estimation Based on Stratified Sampling Using Multiplicative Bias Correction Approach

In the context of stratified sampling, we develop a nonparametric regression technique to estimating finite population quantiles in model-based frameworks using a multiplicative bias correction strategy. Furthermore, the proposed estimator's asymptotic behavior is presented, and when certain conditions are met, the estimator is observed to be asymptotically unbiased and asymptotically consistent. Simulation studies were conducted to determine the proposed estimator's performance for the three quartiles of two fictitious populations under varied distributional assumptions. Based on relative biases, mean-squared errors, and relative root-mean-squared errors, the proposed estimator can be extremely satisfactory, according to these findings.

Automated summary

In the context of stratified sampling, a nonparametric regression technique was developed to estimate finite population quantiles in model-based frameworks. The proposed estimator was found to have good asymptotic behavior under certain conditions.