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• W2165009258 abstract "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." @default.
• W2165009258 created "2016-06-24" @default.
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• W2165009258 date "2013-08-12" @default.
• W2165009258 modified "2023-10-17" @default.
• W2165009258 title "The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes" @default.
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• W2165009258 doi "https://doi.org/10.1111/rssb.12033" @default.
• W2165009258 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4012833" @default.
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