Aligned Rank Statistics for Repeated Measurement Models with Orthonormal Design, Employing a Chernoff-Savage Approach

In this paper, aligned rank statistics are considered for testing hypotheses regarding the location in repeated measurement designs, where the design matrix for each set of measurements is orthonormal. Such a design may, for instance, be used when testing for linearity in a partially linear model. It turns out that the centered design matrix is not of full rank, and, therefore, doesn't quite satisfy the usual conditions in the literature. The number of degrees of freedom of the limiting chi-squared distribution of the test statistic under the null hypothesis, however, is not affected, unless rather special hypotheses are tested. An independent derivation of this limiting distribution is given, using the Chernoff-Savage approach. In passing, it is observed that independence of the choice of aligner, which in the location problem is well-known to be due to cancellation, may in scale problems occur as a result of the type of score function suitable for scale tests. A possible extension to multivariate data is briefly indicated