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Title: Regularized joint estimation of related vector autoregressive models
In a number of applications, one has access to high-dimensional time series data on several related subjects. A motivating application area comes from the neuroimaging field, such as brain fMRI time series data, obtained from various groups of subjects (cases/controls) with a specific neurological disorder. The problem of regularized joint estimation of multiple related Vector Autoregressive (VAR) models is discussed, leveraging a group lasso penalty in addition to a regular lasso one, so as to increase statistical efficiency of the estimates by borrowing strength across the models. A modeling framework is developed that it allows for both group-level and subject-specific effects for related subjects, using a group lasso penalty to estimate the former. An estimation procedure is introduced, whose performance is illustrated on synthetic data and compared to other state-of-the-art methods. Moreover, the proposed approach is employed for the analysis of resting state fMRI data. In particular, a group-level descriptive analysis is conducted for brain inter-regional temporal effects of Attention Deficit Hyperactive Disorder (ADHD) patients as opposed to controls, with the data available from the ADHD-200 Global Competition repository.  more » « less
Award ID(s):
1821220
PAR ID:
10108540
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Computational statistics & data analysis
Volume:
139
ISSN:
1872-7352
Page Range / eLocation ID:
164-177
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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