  Nonparametric tests for functional data are a challenging class of tests to
work with because of the potentially high dimensional nature of the data. One
of the main challenges for considering rank-based tests, like the Mann-Whitney
or Wilcoxon Rank Sum tests (MWW), is that the unit of observation is typically
a curve. Thus any rank-based test must consider ways of ranking curves. While
several procedures, including depth-based methods, have recently been used to
create scores for rank-based tests, these scores are not constructed under the
null and often introduce additional, uncontrolled for variability. We therefore
reconsider the problem of rank-based tests for functional data and develop an
alternative approach that incorporates the null hypothesis throughout. Our
approach first ranks realizations from the curves at each measurement
occurrence, then calculates a summary statistic for the ranks of each subject,
and finally re-ranks the summary statistic in a procedure we refer to as a
doubly ranked test. We propose two summaries for the middle step: a sufficient
statistic and the average rank. As we demonstrate, doubly rank tests are more
powerful while maintaining ideal type I error in the two sample, MWW setting.
We also extend our framework to more than two samples, developing a
Kruskal-Wallis test for functional data which exhibits good test
characteristics as well. Finally, we illustrate the use of doubly ranked tests
in functional data contexts from material science, climatology, and public
health policy.
