Statistical inference using debiased group graphical lasso for multiple sparse precision matrices
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908577840496640 |
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| author | Bhowal, Sayan Ranjan Paul, Debashis Basak, Gopal K Das, Samarjit |
| author_facet | Bhowal, Sayan Ranjan Paul, Debashis Basak, Gopal K Das, Samarjit |
| contents | Debiasing group graphical lasso estimates enables statistical inference when multiple Gaussian graphical models share a common sparsity pattern. We analyze the estimation properties of group graphical lasso, establishing convergence rates and model selection consistency under irrepresentability conditions. Based on these results, we construct debiased estimators that are asymptotically Gaussian, allowing hypothesis testing for linear combinations of precision matrix entries across populations. We also investigate regimes where irrepresentibility conditions does not hold, showing that consistency can still be attained in moderately high-dimensional settings. Simulation studies confirm the theoretical results, and applications to real datasets demonstrate the practical utility of the method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04683 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistical inference using debiased group graphical lasso for multiple sparse precision matrices Bhowal, Sayan Ranjan Paul, Debashis Basak, Gopal K Das, Samarjit Statistics Theory Applications Debiasing group graphical lasso estimates enables statistical inference when multiple Gaussian graphical models share a common sparsity pattern. We analyze the estimation properties of group graphical lasso, establishing convergence rates and model selection consistency under irrepresentability conditions. Based on these results, we construct debiased estimators that are asymptotically Gaussian, allowing hypothesis testing for linear combinations of precision matrix entries across populations. We also investigate regimes where irrepresentibility conditions does not hold, showing that consistency can still be attained in moderately high-dimensional settings. Simulation studies confirm the theoretical results, and applications to real datasets demonstrate the practical utility of the method. |
| title | Statistical inference using debiased group graphical lasso for multiple sparse precision matrices |
| topic | Statistics Theory Applications |
| url | https://arxiv.org/abs/2510.04683 |