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High-dimensional graphs and variable selection with the Lasso

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...

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Publication status:
Published

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Publisher copy:
10.1214/009053606000000281

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Role:
Author
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

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