The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
Open accessAbstract
Summary: 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.
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Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Lauritzen, S. L. (1996) · doi:10.1093/oso/9780198522195.001.0001 · Cited by 2,976
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A Network Analysis Approach on the Psychological Flexibility/Inflexibility Model
Christodoulou, A., Karekla, M., Costantini, G., & Michaelides, M. P. (2023) · Behavior Therapy · doi:10.1016/j.beth.2023.01.002 · Cited by 22
Fused graphical lasso recovers flowering time mutation genes in arabidopsis thaliana
Kapoor, R., Datta, A., & Thomson, M. (2021) · Inventions · doi:10.3390/inventions6030052 · Cited by 5
Comparing symptom networks of daytime and nocturnal panic attacks in a community-based sample
Smith, N. S., Bauer, B. W., & Capron, D. W. (2022) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2021.102514 · Cited by 4
Metadata
- Authors
- Danaher, P., Wang, P., & Witten, D. M.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-08-12
- Volume
- 76
- Issue
- 2
- Pages
- 373-397
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2165009258
- Cited by
- 855
- References
- 4
- FWCI
- 45.88
- Type
- journal article
- Language
- en
- Open access
- bronze
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