  The use of principal component methods to analyze functional data is
appropriate in a wide range of different settings. In studies of ``functional
data analysis,'' it has often been assumed that a sample of random functions is
observed precisely, in the continuum and without noise. While this has been the
traditional setting for functional data analysis, in the context of
longitudinal data analysis a random function typically represents a patient, or
subject, who is observed at only a small number of randomly distributed points,
with nonnegligible measurement error. Nevertheless, essentially the same
methods can be used in both these cases, as well as in the vast number of
settings that lie between them. How is performance affected by the sampling
plan? In this paper we answer that question. We show that if there is a sample
of $n$ functions, or subjects, then estimation of eigenvalues is a
semiparametric problem, with root-$n$ consistent estimators, even if only a few
observations are made of each function, and if each observation is encumbered
by noise. However, estimation of eigenfunctions becomes a nonparametric problem
when observations are sparse. The optimal convergence rates in this case are
those which pertain to more familiar function-estimation settings. We also
describe the effects of sampling at regularly spaced points, as opposed to
random points. In particular, it is shown that there are often advantages in
sampling randomly. However, even in the case of noisy data there is a threshold
sampling rate (depending on the number of functions treated) above which the
rate of sampling (either randomly or regularly) has negligible impact on
estimator performance, no matter whether eigenfunctions or eigenvectors are
being estimated.
