A randomization-based perspective on analysis of variance: a test statistic robust to treatment effect heterogeneity

• 成果类型: Article
• 署名作者: Ding, Peng; Dasgupta, Tirthankar
• 署名单位: University of California System; University of California Berkeley; Rutgers University System; Rutgers University New Brunswick
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx059
• 发表日期: 2018
• 页码: 4556
• 关键词: permutation tests; bootstrap; anova

摘要

Fisher randomization tests for Neyman's null hypothesis of no average treatment effect are considered in a finite-population setting associated with completely randomized experiments involving more than two treatments. The consequences of using the F statistic to conduct such a test are examined, and we argue that under treatment effect heterogeneity, use of the F statistic in the Fisher randomization test can severely inflate the Type I error under Neyman's null hypothesis. We propose to use an alternative test statistic, derive its asymptotic distributions under Fisher's and Neyman's null hypotheses, and demonstrate its advantages through simulations.