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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2502.17684 |
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| _version_ | 1866916628879376384 |
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| author | Wang, Jiacheng Gao, Xin |
| author_facet | Wang, Jiacheng Gao, Xin |
| contents | Motivated by dynamic biologic network analysis, we propose a covariate-dependent Gaussian graphical model (cdexGGM) for capturing network structure that varies with covariates through a novel parameterization. Utilizing a likelihood framework, our methodology jointly estimates all dynamic edge and vertex parameters. We further develop statistical inference procedures to test the dynamic nature of the underlying network. Concerning large-scale networks, we perform composite likelihood estimation with an $\ell_1$ penalty to discover sparse dynamic network structures. We establish the estimation error bound in $\ell_2$ norm and validate the sign consistency in the high-dimensional context. We apply our method to an influenza vaccine data set to model the dynamic gene network that evolves with time. We also investigate a Down syndrome data set to model the dynamic protein network which varies under a factorial experimental design. These applications demonstrate the applicability and effectiveness of the proposed model. The supplemental materials for this article are available online. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17684 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Dimensional Covariate-Dependent Gaussian Graphical Models Wang, Jiacheng Gao, Xin Methodology Motivated by dynamic biologic network analysis, we propose a covariate-dependent Gaussian graphical model (cdexGGM) for capturing network structure that varies with covariates through a novel parameterization. Utilizing a likelihood framework, our methodology jointly estimates all dynamic edge and vertex parameters. We further develop statistical inference procedures to test the dynamic nature of the underlying network. Concerning large-scale networks, we perform composite likelihood estimation with an $\ell_1$ penalty to discover sparse dynamic network structures. We establish the estimation error bound in $\ell_2$ norm and validate the sign consistency in the high-dimensional context. We apply our method to an influenza vaccine data set to model the dynamic gene network that evolves with time. We also investigate a Down syndrome data set to model the dynamic protein network which varies under a factorial experimental design. These applications demonstrate the applicability and effectiveness of the proposed model. The supplemental materials for this article are available online. |
| title | High-Dimensional Covariate-Dependent Gaussian Graphical Models |
| topic | Methodology |
| url | https://arxiv.org/abs/2502.17684 |