Recovering gradients from sparsely observed functional data.

The recovery of gradients of sparsely observed functional data is a challenging ill-posed inverse problem. Given observations of smooth curves (e.g., growth curves) at isolated time points, the aim is to provide estimates of the underlying gradients (or growth velocities). To address this problem, we develop a Bayesian inversion approach that models the gradient in the gaps between the observation times by a tied-down Brownian motion, conditionally on its values at the observation times. The posterior mean and covariance kernel of the growth velocities are then found to have explicit and computationally tractable representations in terms of quadratic splines. The hyperparameters in the prior are specified via nonparametric empirical Bayes, with the prior precision matrix at the observation times estimated by constrained ℓ₁ minimization. The infinitessimal variance of the Brownian motion prior is selected by cross-validation. The approach is illustrated using both simulated and real data examples.