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Article
On Stratified Bivariate Ranked Set Sampling with Optimal Allocation for Naive and Ratio Estimators
Journal of Applied Statistics
  • Lili Yu, Georgia Southern University
  • Hani M. Samawi, Georgia Southern University
  • Daniel Linder, Georgia Southern University
  • Arpita Chatterjee, Georgia Southern University
  • Yisong Huang, Georgia Southern University
  • Robert L. Vogel, Georgia Southern University
Document Type
Article
Publication Date
5-11-2016
DOI
10.1080/02664763.2016.1177495
Disciplines
Abstract
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.
Citation Information
Lili Yu, Hani M. Samawi, Daniel Linder, Arpita Chatterjee, et al.. "On Stratified Bivariate Ranked Set Sampling with Optimal Allocation for Naive and Ratio Estimators" Journal of Applied Statistics (2016)
Available at: http://works.bepress.com/robert_vogel/74/