On Stratified Bivariate Ranked Set Sampling for Regression Estimators

Authors

Daniel F. Linder, Georgia Southern UniversityFollow
Hani Samawi, Georgia Southern UniversityFollow
Lili Yu, Georgia Southern UniversityFollow
Arpita Chatterjee, Georgia Southern UniversityFollow
Yisong Huang, Georgia Southern UniversityFollow
Robert L. Vogel, Georgia Southern UniversityFollow

Document Type

Article

Publication Date

2015

Publication Title

Journal of Applied Statistics

DOI

10.1080/02664763.2015.1043868

ISSN

1360-0532

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.

Recommended Citation

Linder, Daniel F., Hani Samawi, Lili Yu, Arpita Chatterjee, Yisong Huang, Robert L. Vogel. 2015. "On Stratified Bivariate Ranked Set Sampling for Regression Estimators." Journal of Applied Statistics, 42 (12): 2471-2483. doi: 10.1080/02664763.2015.1043868
https://digitalcommons.georgiasouthern.edu/biostat-facpubs/92