In many studies, a researcher attempts to describe a population where units are measured for multiple outcomes, or responses. In this paper, researchers present an efficient procedure based on ranked set sampling to estimate and perform hypothesis testing on a multivariate mean. The method is based on ranking on an auxiliary covariate, which is assumed to be correlated with the multivariate response, in order to improve the efficiency of the estimation. Researchers showed that the proposed estimators developed under this sampling scheme are unbiased, have smaller variance in the multivariate sense, and are asymptotically Gaussian. They also demonstrated that the efficiency of multivariate regression estimator can be improved by using Ranked set sampling. A bootstrap routine is developed in the statistical software R to perform inference when the sample size is small. Researchers use a simulation study to investigate the performance of the method under known conditions and apply the method to the biomarker data collected in China Health and Nutrition Survey (CHNS 2009) data.
”On Inference of Multivariate Means Under Ranked Set Sampling,” was recently published in the Communications for Statistical Applications and Methods.
Dr. Haresh Rochani, assistant professor and director of the Karl E. Peace Center for Biostatistics at the Georgia Southern University Jiann-Ping Hsu College of Public Health(JPHCOPH) was the lead author. Dr. Hani Samawi, professor of biostatistics at JPHCOPH, Dr. Daniel Linder, Augusta University, and Dr. Viral Panchal, JPHCOPH biostatistics alumni were co-authors.