Regression Estimators Using Stratified Ranked Set Sampling

Arpita Chatterjee, Georgia Southern University
Hani M. Samawi, Georgia Southern University
Lili Yu
Daniel F. Linder, Georgia Southern University
Jingxian Cai, Georgia Southern University


This article is intended to investigate the performance of two types of stratified regression estimators, namely the separate and the combined estimator, using stratified ranked set sampling (SRSS), introduced by Samawi (1996). The expressions for mean and variance of the proposed estimates are derived and are shown to be unbiased. A simulation study is designed to compare the efficiency of SRSS relative to other sampling procedure under varying model scenarios. Our investigation indicates that the regression estimator of the population mean obtained through an SRSS becomes more efficient than the crude sample mean estimator using stratified simple random sampling. These findings are also illustrated with the help of a data set on bilirubin levels in babies in a neonatal intensive care unit.