On inference of multivariate means under ranked set sampling
| Autor: | Viral Panchal, Hani M. Samawi, Haresh Rochani, Daniel F. Linder |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
education.field_of_study Multivariate statistics Applied Mathematics Population Inference Estimator 030209 endocrinology & metabolism 01 natural sciences 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Ranking Sample size determination Modeling and Simulation Statistics Covariate 0101 mathematics Statistics Probability and Uncertainty education Finance Mathematics Statistical hypothesis testing |
| Zdroj: | Communications for Statistical Applications and Methods. 25:1-13 |
| ISSN: | 2383-4757 |
| DOI: | 10.29220/csam.2018.25.1.001 |
| Popis: | In many studies, a researcher attempts to describe a population where units are measured for multiple outcomes, or responses. In this paper, we 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. We showed that the proposed estimators developed under this sampling scheme are unbiased, have smaller variance in the multivariate sense, and are asymptotically Gaussian. We 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. 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. |
| Databáze: | OpenAIRE |
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