期刊
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
卷 78, 期 2, 页码 487-504出版社
WILEY
DOI: 10.1111/rssb.12123
关键词
Conditional independence; Graphical model; High dimensional data; Rate of convergence; Time series
资金
- NHLBI NIH HHS [R01 HL123407] Funding Source: Medline
- NIBIB NIH HHS [P41 EB015909, R01 EB012547] Funding Source: Medline
- NIMH NIH HHS [R01 MH102339] Funding Source: Medline
- NINDS NIH HHS [R01 NS060910] Funding Source: Medline
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1546462, 1408910] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [GRANTS:14023950] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1454377] Funding Source: National Science Foundation
We consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which consists of T possibly dependent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of closeness between subjects. We propose a kernel-based method for jointly estimating all graphical models. Theoretically, under a double asymptotic framework, where both (T,n) and the dimension d can increase, we provide an explicit rate of convergence in parameter estimation. It characterizes the strength that one can borrow across different individuals and the effect of data dependence on parameter estimation. Empirically, experiments on both synthetic and real resting state functional magnetic resonance imaging data illustrate the effectiveness of the method proposed.
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