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

Summary: Kernel-based estimators of a quantile function using stratified ranked set sampling and stratified simple random sampling methods are proposed. Theoretical and simulation studies show that the efficiency of the estimator based on stratified ranked set sampling is higher compared to the counterpart based on stratified simple random sampling.