Nonparametric two-sample methods for ranked-set sample data

• 成果类型: Article
• 署名作者: Fligner, Michael A.; MacEachern, Steven N.
• 署名单位: University System of Ohio; Ohio State University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214506000000410
• 发表日期: 2006
• 页码: 1107-1118
• 关键词: judgment; statistics; rankings

摘要

Anew collection of procedures is developed for the analysis of two-sample, ranked-set samples, providing an alternative to the Bohn-Wolfe procedure. These procedures split the data based on the ranks in the ranked-set sample and lead to tests for the centers of distributions, confidence intervals, and point estimators. The advantages of the new tests are that they require essentially no assumptions about the mechanism by which rankings are imperfect, that they maintain their level whether rankings are perfect or imperfect, that they lead to generalizations of the Bohn-Wolfe procedure that can be used to increase power in the case of perfect rankings, and that they allow one to analyze both balanced and unbalanced ranked-set samples. A new class of imperfect ranking models is proposed, and the performance of the procedure is investigated under these models. When rankings are random, a theorem is presented which characterizes efficient data splits. Because random rankings are equivalent to iid samples, this theorem applies to a wide class of statistics and has implications for a variety of computationally intensive methods.