A randomization-based perspective of analysis of variance: a test statistic robust to treatment effect heterogeneity
Statistics Theory
2017-07-26 v2 Methodology
Statistics Theory
Abstract
Fisher randomization tests for Neyman's null hypothesis of no average treatment effects are considered in a finite population setting associated with completely randomized experiments with more than two treatments. The consequences of using the statistic to conduct such a test are examined both theoretically and computationally, and it is argued that under treatment effect heterogeneity, use of the statistic in the Fisher randomization test can severely inflate the type I error under Neyman's null hypothesis. An alternative test statistic is proposed, its asymptotic distributions under Fisher's and Neyman's null hypotheses are derived, and its advantages demonstrated.
Keywords
Cite
@article{arxiv.1608.01787,
title = {A randomization-based perspective of analysis of variance: a test statistic robust to treatment effect heterogeneity},
author = {Peng Ding and Tirthankar Dasgupta},
journal= {arXiv preprint arXiv:1608.01787},
year = {2017}
}