  The problem of estimating the mean of random functions based on discretely
sampled data arises naturally in functional data analysis. In this paper, we
study optimal estimation of the mean function under both common and independent
designs. Minimax rates of convergence are established and easily implementable
rate-optimal estimators are introduced. The analysis reveals interesting and
different phase transition phenomena in the two cases. Under the common design,
the sampling frequency solely determines the optimal rate of convergence when
it is relatively small and the sampling frequency has no effect on the optimal
rate when it is large. On the other hand, under the independent design, the
optimal rate of convergence is determined jointly by the sampling frequency and
the number of curves when the sampling frequency is relatively small. When it
is large, the sampling frequency has no effect on the optimal rate. Another
interesting contrast between the two settings is that smoothing is necessary
under the independent design, while, somewhat surprisingly, it is not essential
under the common design.
