Jerome H. Friedman
= Jerome H. Friedman
{c}
<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.<Sparse inverse covariance estimation with the graphical lasso by Jerome H. Friedman and others>[Original byline, abstract and 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>.<Sparse inverse covariance estimation with the graphical lasso by Jerome H. Friedman and others>[Precision-matrix interpretation and algorithm]{ref}