  For general repeated measures designs the Wald-type statistic (WTS) is an
asymptotically valid procedure allowing for unequal covariance matrices and
possibly non-normal multivariate observations. The drawback of this procedure
is the poor performance for small to moderate samples, i.e. decisions based on
the WTS may become quite liberal. It is the aim of the present paper to improve
its small sample behavior by means of a novel permutation procedure. In
particular, it is shown that a permutation version of the WTS inherits its good
large sample properties while yielding a very accurate finite sample control of
the type-I error as shown in extensive simulations. Moreover, the new
permutation method is motivated by a practical data set of a split plot design
with a factorial structure on the repeated measures.
