TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models
We propose a new procedure for optimally estimating high dimensional Gaussian graphical models. Our approach is asymptotically tuning-free and non-asymptotically tuning-insensitive: It requires very little effort to choose the tuning parameter in finite sample settings. Computationally, our procedur...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Mathematical Statistics,
2018-03-19T17:51:15Z.
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| Subjects: | |
| Online Access: | Get fulltext |