Eardi Lila

In this talk, we introduce a framework for the statistical analysis of functional data in a setting where these objects cannot be fully observed, but only indirect and noisy measurements are available, namely an inverse problem setting. The samples can either be unconstrained functional data or objects living in non-Euclidean spaces, such as covariance operator. To illustrate the proposed ideas, we will show an application of the proposed model to medical imaging.