Structural components in functional data

Analyzing functional data often leads to finding common factors, for which functional principal components analysis proves to be a useful tool to summarize and characterize the random variation in a function space. The representation in terms of eigenfunctions is optimal in the sense of L2 approximation. However, the eigenfuntions are not always directed towards an interesting and interpretable direction in the context of functional data and thus could obscure the underlying structure. This paper proposes an alternative to functional principal component analysis that produces directed components which may be more informative and easier to interpret. These structural components are similar to principal components, but are adapted to situations in which the domain of the function may be decomposed into disjoint intervals such that there is effectively independence between intervals and positive correlation within intervals. The approach is demonstrated with examples as well as real data. Properties for special cases are also studied.