Multivariate Mean Estimation Under Efficient Sampling Designs

Presenters/Authors

Daniel F. Linder, Georgia Southern UniversityFollow
Haresh Rochani, Georgia Southern UniversityFollow
Hani M. Samawi, Georgia Southern UniversityFollow
Viral Panchal, Georgia Southern UniversityFollow

Document Type

Presentation

Presentation Date

3-9-2016

Abstract or Description

In many studies, the researchers attempt to describe the relationship between more than two outcome (or response) variables with its determinants (covariates). In this paper, we present an efficient procedure based on ranked set sampling to estimate and perform the hypothesis testing on a multivariate outcome 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. We show that the proposed estimator developed under this sampling scheme is unbiased, has smaller variance in the multivariate sense, and is asymptotically Gaussian. A bootstrap routine is developed in the statistical software R to perform the inference when the sample size is small. We also extend the regression estimators based on ranked set sampling to multivariate regression. We 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.

Sponsorship/Conference/Institution

Eastern North American Region International Biometric Society Spring Meeting (ENAR)

Location

Austin, TX

Recommended Citation

Linder, Daniel F., Haresh Rochani, Hani M. Samawi, Viral Panchal. 2016. "Multivariate Mean Estimation Under Efficient Sampling Designs." Biostatistics Faculty Presentations. Presentation 5.
https://digitalcommons.georgiasouthern.edu/biostat-facpres/5