joint graphical lasso for inverse covariance estimation across multiple classes

We consider the problem of estimating multiple related Gaussian graphical models from a high dimensional data set with observations belonging to distinct classes. We propose the joint graphical lasso, which borrows strength across the classes to estimate multiple graphical models that share certain...

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Vydáno v:	Journal of the Royal Statistical Society. Series B, Statistical methodology Ročník 76; číslo 2; s. 373 - 397
Hlavní autoři:	Danaher, Patrick, Wang, Pei, Witten, Daniela M
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Blackwell Publishers 01.03.2014 Blackwell Publishing Ltd John Wiley & Sons Ltd Oxford University Press
Témata:	Algorithms Alternating directions method of multipliers Analysis of covariance Covariance data collection Gaussian graphical model Generalization Generalized fused lasso Graph theory Graphical lasso Graphical models Group lasso High dimensional data Multiplier multipliers Network estimation Optimization Penalties Probability Statistical methods Statistics Studies high-dimensional generalized fused lasso Gaussian graphical model alternating directions method of multipliers graphical lasso group lasso network estimation
ISSN:	1369-7412, 1467-9868
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Popis
Shrnutí:	We consider the problem of estimating multiple related Gaussian graphical models from a high dimensional data set with observations belonging to distinct classes. We propose the joint graphical lasso, which borrows strength across the classes to estimate multiple graphical models that share certain characteristics, such as the locations or weights of non‐zero edges. Our approach is based on maximizing a penalized log‐likelihood. We employ generalized fused lasso or group lasso penalties and implement a fast alternating directions method of multipliers algorithm to solve the corresponding convex optimization problems. The performance of the method proposed is illustrated through simulated and real data examples.
Bibliografie:	http://dx.doi.org/10.1111/rssb.12033 istex:0BDA33974C3C9B708B7C4B7942FC7DEE8E50B850 'Benchmarking the two MCMC strategies for sampling the Bayesian bridge posterior distiution' and 'An empirical study of mixing rates in parameter-expanded Gibbs samplers for sparse Bayesian regression models' ArticleID:RSSB12033 National Institutes of Health - No. 1R01GM082802; No. P01CA53996; No. U24CA086368; No. DP5OD009145 ark:/67375/WNG-T9FSVP5K-1 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	1369-7412 1467-9868
DOI:	10.1111/rssb.12033