On stratified bivariate ranked set sampling with optimal allocation for naïve and ratio estimators

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

Research output: Contribution to journalArticle


The purpose of the current work is to introduce stratified bivariate ranked set sampling (SBVRSS) and investigate its performance for estimating the population mean using both naïve and ratio methods. The properties of the proposed estimator are derived along with the optimal allocation with respect to stratification. We conduct a simulation study to demonstrate the relative efficiency of SBVRSS as compared to stratified bivariate simple random sampling (SBVSRS) for ratio estimation. Data that consist of weights and bilirubin levels in the blood of 120 babies are used to illustrate the procedure on a real data set. Based on our simulation, SBVRSS for ratio estimation is more efficient than using SBVSRS in all cases.

Original languageEnglish (US)
Pages (from-to)457-473
Number of pages17
JournalJournal of Applied Statistics
Issue number3
StatePublished - Feb 17 2017
Externally publishedYes



  • Stratified bivariate ranked set sampling
  • naïve estimator
  • optimal allocation
  • ranked set sampling
  • ratio estimator

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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