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Presentation
(x) Regression Estimators Using Stratified Ranked Set Sampling
ICSA-KISS-2014 Applied Statistics Symposium (2014)
  • Arbita Chatterjee, Georgia Southern University
  • Hani Samawi, Georgia Southern University
  • Lili Yu, Georgia Southern University
  • Daniel Linder, Georgia Southern University
  • Jingxian Cai, Georgia Southern University
  • Robert Vogel, Georgia Southern University
Abstract
This article is intended to investigate the performance of two types of stratified regression estimators, namely the separate and the combined estimator, using 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 ther 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.
Keywords
  • Ranked set sampling,
  • Stratified ranked set sampling,
  • Regression estimator
Disciplines
Publication Date
June 16, 2014
Citation Information
Arbita Chatterjee, Hani Samawi, Lili Yu, Daniel Linder, et al.. "(x) Regression Estimators Using Stratified Ranked Set Sampling" ICSA-KISS-2014 Applied Statistics Symposium (2014)
Available at: http://works.bepress.com/daniel_linder/10/