Covariate-Matched One-Sided Tests for the Difference between Functional Means
协变量匹配的函数均值差单侧检验
When testing hypotheses about the effects of different treatments, variation among covariates can become confounded with that between treatments unless the treatments are applied using paired covariates. In the context of unpaired covariates, we propose implicit covariate-matching methods for testing the hypothesis that one treatment effect is greater than another. The methods are founded on the assumption that the mean treatment effect, conditional on the covariate, is a…