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Joined 2026-09-21 Message
Incoming links: Robert Tibshirani
Jerome H. Friedman 2026-11-05
Jerome H. Friedman is the statistician who coauthored the Graphical Lasso with Trevor Hastie and Robert Tibshirani. Their paper estimates a sparse inverse covariance matrix using penalized likelihood and a computationally efficient iterative algorithm.[ref]
The statistical benefit of a sparse precision matrix is an interpretable pattern of conditional relationships. In a multivariate normal distribution, an off-diagonal zero means that the corresponding variables are conditionally independent given the others. This conditional relationship can differ from their ordinary marginal correlation. The paper's repeated Lasso calculations connect that model with practical convex optimization.[ref]
Robert Tibshirani 2026-11-05
Robert Tibshirani develops methods for statistical learning and biostatistics, with applications to genomics and medicine. His Stanford research profile connects methodological work with scientific data and computational tools.[ref]
With Jerome H. Friedman and Trevor Hastie, he developed the Graphical Lasso for estimating a sparse precision matrix. An absolute-value penalty encourages zero entries, which encode conditional independence relationships in a multivariate normal distribution. The method combines a statistical model with an efficient optimization algorithm.[ref]
Sparse inverse covariance estimation with the graphical lasso by Jerome H. Friedman and others. Abstract and penalized likelihood formulation
Sparse inverse covariance estimation with the graphical lasso by Jerome H. Friedman and others 2026-11-05
Biostatistics 9(3), 432–441; DOI: 10.1093/biostatistics/kxm045. Online 12 December 2007. Equations 2.1, 2.6 and Remark 2.2. Statistician Jerome H. Friedman verified by Stanford Statistics faculty listing; distinct from physicist Jerome Isaac Friedman.
Trevor Hastie 2026-11-05
Trevor Hastie develops methods in statistical learning, including generalized additive models and regularized regression. His research page describes the interaction between interpretable statistical models, flexible prediction and efficient computation.[ref]
His joint Graphical Lasso work with Jerome H. Friedman and Robert Tibshirani turns sparse precision matrix estimation into repeated Lasso subproblems. A blockwise algorithm updates parts of the estimate while solving the penalized likelihood problem, making large conditional-dependence models computationally feasible.[ref]
Sparse inverse covariance estimation with the graphical lasso by Jerome H. Friedman and others. Algorithm development and coordinate-descent formulation