Cookies?
Library Header Image
LSE Research Online LSE Library Services

Doubly functional graphical models in high dimensions

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

[img] Text (Doubly functional graphical models in high dimensions) - Accepted Version
Repository staff only until 11 February 2021.

Download (1MB) | Request a copy

Identification Number: 10.1093/biomet/asz072

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: 06 Jul 2020 07:30
URI: http://eprints.lse.ac.uk/id/eprint/103120

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics