On stratified bivariate ranked set sampling for regression estimators

Daniel F Linder, Hani Samawi, Lili Yu, Arpita Chatterjee, Yisong Huang, Robert Vogel

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2571-2583
Number of pages13
JournalJournal of Applied Statistics
Issue number12
StatePublished - Dec 2 2015


  • bivariate ranked set sampling
  • ranked set sampling
  • ratio estimator
  • regression estimator
  • stratified sampling

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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