  In this article we present some statistical applications of the functional
singular value decomposition (FSVD). This tool allows us to decompose the
sample mean of a bivariate stochastic process into components that are
functions of separate variables. These components are sometimes interpretable
functions that summarize salient features of the data. The FSVD can be used to
visually detect outliers, to estimate the mean of a stochastic process or to
obtain individual smoothers of the sample surfaces. As estimators of the mean,
we show by simulation that FSVD estimators are competitive with tensor-product
splines in some situations.
