We introduce a novel class of models for functional data exhibiting skewness or other shape characteristics that vary with spatial location. Such data are not envisaged by the current approaches to model functional data, due to the lack of Gaussian ? like features. Our methodology allows modeling the pointwise quantiles, has interpretability advantages and is computationally feasible. The methods were motivated by and are illustrated with a state-of-the-art study of neuronal tracts in multiple sclerosis patients and healthy controls.
Group for Research in Decision Analysis