Doubly functional graphical models in high dimensions

Qiao, Xinghao ORCID: 0000-0002-6546-6595, Qian, Cheng, James, Gareth M. and Guo, Shaojun (2020) Doubly functional graphical models in high dimensions. Biometrika, 107 (2). 415 - 431. ISSN 0006-3444

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Abstract

We consider estimating a functional graphical model from multivariate functional observations. In functional data analysis, the classical assumption is that each function has been measured over a densely sampled grid. However, in practice the functions have often been observed, with measurement error, at a relatively small number of points. We propose a class of doubly functional graphical models to capture the evolving conditional dependence relationship among a large number of sparsely or densely sampled functions. Our approach first implements a nonparametric smoother to perform functional principal components analysis for each curve, then estimates a functional covariance matrix and finally computes sparse precision matrices, which in turn provide the doubly functional graphical model. We derive some novel concentration bounds, uniform convergence rates and model selection properties of our estimator for both sparsely and densely sampled functional data in the high-dimensional large-$p$, small-$n$ regime. We demonstrate via simulations that the proposed method significantly outperforms possible competitors. Our proposed method is applied to a brain imaging dataset.

Item Type:	Article
Official URL:	https://academic.oup.com/biomet
Additional Information:	© 2020 Biometrika Trust
Divisions:	Statistics Mathematics
Subjects:	Q Science > QA Mathematics H Social Sciences > HA Statistics
Date Deposited:	20 Jan 2020 11:57
Last Modified:	01 Nov 2024 19:33
URI:	http://eprints.lse.ac.uk/id/eprint/103120

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