Name
Abstract | ||
|---|---|---|
Ranked set sampling (RSS) is a sampling approach that can produce improved statistical inference when the ranking process is perfect. While some inferential RSS methods are robust to imperfect rankings, other methods may fail entirely or provide less efficiency. We develop a nonparametric procedure to assess whether the rankings of a given RSS are perfect. We generate pseudo-samples with a known ranking and use them to compare with the ranking of the given RSS sample. This is a general approach that can accommodate any type of raking, including perfect ranking. To generate pseudo-samples, we consider the given sample as the population and generate a perfect RSS. The test statistics can easily be implemented for balanced and unbalanced RSS. The proposed tests are compared using Monte Carlo simulation under different distributions and applied to a real data set. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s00180-016-0698-7 | Computational Statistics |
Keywords | DocType | Volume |
Imperfect rankings, Order statistics, Ranked set sampling, Resampling | Journal | 32 |
Issue | ISSN | Citations |
4 | 1613-9658 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saeid Amiri | 1 | 0 | 0.68 |
| Reza Modarres | 2 | 40 | 9.30 |
| Silvelyn Zwanzig | 3 | 1 | 2.01 |