On Stratified Ranked Set Sampling for Regression Estimators

Authors

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

Document Type

Article

Publication Date

1-3-2017

Publication Title

Journal of Statistics and Management Systems

DOI

10.1080/09720510.2017.1411027

ISSN

2169-0014

Abstract

Two types of stratified regression estimators for the population mean, the separate and the combined estimators, are investigated using stratified random sampling scheme (SSRS) and stratified ranked set sampling (SRSS). We derived mean and variance of the proposed estimators. In addition, we compared the performance of the regression estimators using SRSS with respect to SSRS by simulation. Our derivations and simulations revealed that our proposed estimators are unbiased and using SRSS is more efficient than using SSRS. The procedure are illustrated by using the bilirubin levels in babies in a neonatal intensive care unit data.

Recommended Citation

Chatterjee, Arpita, Hani Samawi, Lili Yu, Daniel F. Linder, Jingxian Cai, Robert L. Vogel. 2017. "On Stratified Ranked Set Sampling for Regression Estimators." Journal of Statistics and Management Systems, 20 (6): 1147-1165. doi: 10.1080/09720510.2017.1411027
https://digitalcommons.georgiasouthern.edu/biostat-facpubs/217