On Stratified Bivariate Ranked Set Sampling with Optimal Allocation for Naive and Ratio Estimators
The purpose of the current work is to introduce stratified bivariate ranked set sampling (SBVRSS) and investigate its performance for estimating the population means using naive 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 used to illustrate the procedure on a real data set, with our results indicating that SBVRSS for ratio estimation is more efficient than using SBVSRS in all cases presented in the simulations
Eastern North American Region International Biometric Society Annual Conference (ENAR)
Yu, Lili, Hani M. Samawi, Daniel F. Linder, Arpita Chatterjee, Yisong Huang, Robert L. Vogel.
"On Stratified Bivariate Ranked Set Sampling with Optimal Allocation for Naive and Ratio Estimators."
Biostatics Faculty Presentations.