Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies

Multiple-subject network data are fast emerging in recent years, where a separate connectivity matrix is measured over a common set of nodes for each individual subject, along with subject covariates information. In this article, we propose a new generalized matrix response regression model, where t...

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Vydáno v:Journal of computational and graphical statistics Ročník 32; číslo 1; s. 252 - 262
Hlavní autoři: Zhang, Jingfei, Sun, Will Wei, Li, Lexin
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Taylor & Francis 2023
Taylor & Francis Ltd
Témata:
Algorithms
Brain
Computational and statistical errors
Consistency
Generalized linear model
High-dimensional regression
Neuroimaging
Parameter estimation
Regression models
Statistical analysis
Tensors
Neuroimaging
Generalized linear model
High-dimensional regression
Tensors
Computational and statistical errors
ISSN:1061-8600, 1537-2715
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Shrnutí:Multiple-subject network data are fast emerging in recent years, where a separate connectivity matrix is measured over a common set of nodes for each individual subject, along with subject covariates information. In this article, we propose a new generalized matrix response regression model, where the observed network is treated as a matrix-valued response and the subject covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept matrix, and the effect of subject covariates through a sparse slope tensor. We develop an efficient alternating gradient descent algorithm for parameter estimation, and establish the nonasymptotic error bound for the actual estimator from the algorithm, which quantifies the interplay between the computational and statistical errors. We further show the strong consistency for graph community recovery, as well as the edge selection consistency. We demonstrate the efficacy of our method through simulations and two brain connectivity studies. Supplementary materials for this article are available online.
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ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2022.2074434