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Article
On Stratified Bivariate Ranked Set Sampling for Regression Estimators
Journal of Applied Statistics
  • Daniel F. Linder, Georgia Southern University
  • Hani Samawi, Georgia Southern University
  • Lili Yu, Georgia Southern University
  • Arpita Chatterjee, Georgia Southern University
  • Yisong Huang, Georgia Southern University
  • Robert L. Vogel, Georgia Southern University
Document Type
Article
Publication Date
1-1-2015
DOI
10.1080/02664763.2015.1043868
Disciplines
Abstract
We investigate the relative performance of stratified bivariate ranked set sampling (SBVRSS), with respect to stratified simple random sampling (SSRS) for estimating the population mean with regression methods. The mean and variance of the proposed estimators are derived with the mean being shown to be unbiased. We perform a simulation study to compare the relative efficiency of SBVRSS to SSRS under various data-generating scenarios. We also compare the two sampling schemes on a real data set from trauma victims in a hospital setting. The results of our simulation study and the real data illustration indicate that using SBVRSS for regression estimation provides more efficiency than SSRS in most cases.
Citation Information
Daniel F. Linder, Hani Samawi, Lili Yu, Arpita Chatterjee, et al.. "On Stratified Bivariate Ranked Set Sampling for Regression Estimators" Journal of Applied Statistics (2015)
Available at: http://works.bepress.com/robert_vogel/70/