Nonparametric inference for stochastic linear hypotheses: Application to high-dimensional data

• 成果类型: Article
• 署名作者: Kowalski, J; Powell, J
• 署名单位: Johns Hopkins University; Johns Hopkins University; Johns Hopkins Medicine
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/91.2.393
• 发表日期: 2004
• 页码: 393408
• 关键词: depression; models

摘要

The Mann-Whitney-Wilcoxon rank sum test is limited to comparison of two groups with univariate responses. In this paper, we introduce a class of stochastic linear hypotheses that addresses these limitations within a nonparametric setting. We formulate hypotheses for simultaneous comparisons of several, multivariate response groups, without modelling the response distributions. Inference is developed based on U-statistics theory and an exchangeability assumption. The latter condition is required to identify testable hypotheses for high-dimensional response vectors, such as those arising in genomic and psychosocial research. The methodology is illustrated with two real-data applications.