  How can we discern whether the covariance operator of a stochastic process is
of reduced rank, and if so, what its precise rank is? And how can we do so at a
given level of confidence? This question is central to a great deal of methods
for functional data, which require low-dimensional representations whether by
functional PCA or other methods. The difficulty is that the determination is to
be made on the basis of i.i.d. replications of the process observed discretely
and with measurement error contamination. This adds a ridge to the empirical
covariance, obfuscating the underlying dimension. We build a matrix-completion
inspired test statistic that circumvents this issue by measuring the best
possible least square fit of the empirical covariance's off-diagonal elements,
optimised over covariances of given finite rank. For a fixed grid of
sufficiently large size, we determine the statistic's asymptotic null
distribution as the number of replications grows. We then use it to construct a
bootstrap implementation of a stepwise testing procedure controlling the
family-wise error rate corresponding to the collection of hypotheses
formalising the question at hand. Under minimal regularity assumptions we prove
that the procedure is consistent and that its bootstrap implementation is
valid. The procedure circumvents smoothing and associated smoothing parameters,
is indifferent to measurement error heteroskedasticity, and does not assume a
low-noise regime. An extensive simulation study reveals an excellent practical
performance, stably across a wide range of settings, and the procedure is
further illustrated by means of two data analyses.
