Abstract
A depth-based rank sum statistic for multivariate data introduced by Liu and Singh [J. Amer. Statist. Assoc. 88 (1993) 252-260] as an extension of the Wilcoxon rank sum statistic for univariate data has been used in multivariate rank tests in quality control and in experimental studies. Those applications, however, are based on a conjectured limiting distribution, provided by Liu and Singh [J. Amer. Statist. Assoc. 88 (1993) 252-260]. The present paper proves the conjecture under general regularity conditions and, therefore, validates various applications of the rank sum statistic in the literature. The paper also shows that the corresponding rank sum tests can be more powerful than Hotelling's T 2 test and some commonly used multivariate rank tests in detecting location-scale changes in multivariate distributions.
| Original language | English |
|---|---|
| Pages (from-to) | 2879-2896 |
| Number of pages | 18 |
| Journal | Annals of Statistics |
| Volume | 34 |
| Issue number | 6 |
| DOIs | |
| State | Published - Dec 2006 |
Keywords
- Data depth
- Efficiency
- Limiting distribution
- Multivariate data
- Rank sum statistic
- Two-sample problem