- Abstract:
-
The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a computationally attractive alternative to standard covariance selection for sparse high-dimensional graphs. Neighborhood selection estimates the conditional independence restrictions separately fo...
Expand abstract - Publication status:
- Published
- Publisher copy:
- 10.1214/009053606000000281
-
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- Journal:
- Annals of Statistics
- Volume:
- 34
- Issue:
- 3
- Pages:
- 1436-1462
- Publication date:
- 2006-08-01
- DOI:
- ISSN:
-
0090-5364
- URN:
-
uuid:b13c6f22-805b-4a80-b71d-ca59d4bcf815
- Source identifiers:
-
97768
- Local pid:
- pubs:97768
- Language:
- English
- Keywords:
- Copyright date:
- 2006
- Notes:
-
Published at http://dx.doi.org/10.1214/009053606000000281 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org)
Journal article
High-dimensional graphs and variable selection with the Lasso
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