A0226
Title: High-dimensional nonparametric functional graphical models via the functional additive partial correlation operator
Authors: Eftychia Solea - Queen Mary University of London (United Kingdom) [presenting]
Abstract: A novel approach is developed for estimating a nonparametric graphical model for functional data. The approach is built on a new linear operator, the functional additive partial correlation operator, which extends the partial correlation matrix to both the nonparametric and functional settings. We establish both estimation consistency and graph selection consistency of the proposed estimator, while allowing the number of nodes to grow with the increasing sample size. Through simulation studies, we demonstrate that our method performs better than existing methods in cases where the Gaussian or Gaussian copula assumption does not hold. We also demonstrate the performance of the proposed method by a study of an electroencephalography data set to construct a brain network.
