  We consider functional data which are measured on a discrete set of
observation points. Often such data are measured with noise, and then the
target is to recover the underlying signal. Most commonly, practitioners use
some smoothing approach, e.g.,\ kernel smoothing or spline fitting towards this
goal. The drawback of such curve fitting techniques is that they act function
by function, and don't take into account information from the entire sample. In
this paper we argue that signal and noise can be naturally represented as the
common and idiosyncratic component, respectively, of a factor model.
Accordingly, we propose to an estimation scheme which is based on factor
models. The purpose of this paper is to explain the reasoning behind our
approach and to compare its performance on simulated and on real data to
competing methods.
