  In the context of nonparametric regression, we study conditions under which
the consistency (and rates of convergence) of estimators built from discretely
sampled curves can be derived from the consistency of estimators based on the
unobserved whole trajectories. As a consequence, we derive asymptotic results
for most of the regularization techniques used in functional data analysis,
including smoothing and basis representation.
