B0489
Title: Nonparametric and high-dimensional functional graphical models
Authors: Eftychia Solea - Queen Mary University of London (United Kingdom) [presenting]
Holger Dette - Ruhr-Universitaet Bochum (Germany)
Abstract: The problem of constructing nonparametric undirected graphical models for high-dimensional functional data is considered. Most existing statistical methods in this context assume either a Gaussian distribution on the vertices or linear conditional means. We provide a more flexible model which relaxes the linearity assumption by replacing it with an arbitrary additive form. The use of functional principal components offers an estimation strategy that uses a group lasso penalty to estimate the relevant edges of the graph. We establish statistical guarantees for the resulting estimators, which can be used to prove consistency if the dimension and the number of functional principal components diverge to infinity with the sample size. We also investigate the empirical performance of our method through simulation studies and a real data application.
