ANCOVA: A Global Test Based on a Robust Measure of Location or Quantiles When There Is Curvature
نویسنده
چکیده
For two independent groups, let Mj(x) be some conditional measure of location for the jth group associated with some random variable Y , given that some covariate X = x. When Mj(x) is a robust measure of location, or even some conditional quantile of Y , given X, methods have been proposed and studied that are aimed at testing H0: M1(x) = M2(x) that deal with curvature in a flexible manner. In addition, methods have been studied where the goal is to control the probability of one or more Type I errors when testing H0 for each x ∈ {x1, . . . , xp}. This paper suggests a method for testing the global hypothesis H0: M1(x) = M2(x) for ∀x ∈ {x1, . . . , xp} when using a robust or quantile location estimator. An obvious advantage of testing p hypotheses, rather than the global hypothesis, is that it can provide information about where regression lines differ and by how much. But the paper summarizes three general reasons to suspect that testing the global hypothesis can have more power. Data from the Well Elderly 2 study illustrate that testing the global hypothesis can make a practical difference.
منابع مشابه
Robust ANCOVA: Confidence Intervals That Have Some Specified Simultaneous Probability Coverage When There Is Curvature And Two Covariates
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