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.
| Original language | English |
|---|---|
| Pages (from-to) | 2571-2583 |
| Number of pages | 13 |
| Journal | Journal of Applied Statistics |
| Volume | 42 |
| Issue number | 12 |
| DOIs | |
| State | Published - Jan 1 2015 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Scopus Subject Areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Disciplines
- Biostatistics
- Community Health
- Public Health
Keywords
- bivariate ranked set sampling
- ranked set sampling
- ratio estimator
- regression estimator
- stratified sampling
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