Depth for Sparse Functional Data

The notions of depth for functional data provide a way of ordering curves from center-outward. These methods are designed for trajectories that are observed on a fine grid of equally spaced time points. However, in many applications the trajectories are observed on sparse irregularly spaced time points. We propose a model-based consistent procedure for estimating the depths when the curves are observed on sparse and unevenly spaced points.