Authors: Solea, Eftychia
Dette, Holger
Title: Nonparametric and high-dimensional functional graphical models
Language (ISO): en
Abstract: We consider the problem of constructing nonparametric undirected graphical models for highdimensional functional data. Most existing statistical methods in this context assume either a Gaussian distribution on the vertices or linear conditional means. In this article we provide a more flexible model which relaxes the linearity assumption by replacing it by 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.
Subject Headings: undirected graphical models
brain networks
EEG data
lasso
additive models
functional data
URI: http://hdl.handle.net/2003/40101
http://dx.doi.org/10.17877/DE290R-21978
Issue Date: 2021
Appears in Collections:Sonderforschungsbereich (SFB) 823

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