A comprehensive treatment of quadratic-form-based inference in repeated measures designs under diverse asymptotics

Abstract

Split-Plot or Repeated Measures Designs with multiple groups occur naturally in sciences. Their analysis is usually based on the classical Repeated Measures ANOVA. Roughly speaking, the latter can be shown to be asymptotically valid for large sample sizes $n_i$ assuming a fixed number of groups a and time points d. However, for high-dimensional settings with $d>n_i$, this argument breaks down and statistical tests are often based on (standardized) quadratic forms. Furthermore, analysis of their limit behaviour is usually based on certain assumptions on how d converges to ∞ with respect to $n_i$. As this may be hard to argue in practice, we do not want to make such restrictions. Moreover, sometimes also the number of groups a may be large compared to d or $n_i$. To also have an impression about the behaviour of (standardized) quadratic forms as test statistic, we analyze their asymptotics under diverse settings on a, d and $n_i$. In fact, we combine all kinds of combinations, where they diverge or are bounded in a unified framework. To this aim, we assume equal covariance matrices between all groups. Studying the limit distributions in detail, we follow Sattler and Pauly (2018) and propose an approximation to obtain critical values. The resulting test and its approximation approach are investigated in an extensive simulation study focusing on the exceptional asymptotic frameworks that are the main focus of this work.