= Trevor Hastie
{c}
<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.https://hastie.su.domains/research.html[Author research description]{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.<Sparse inverse covariance estimation with the graphical lasso by Jerome H. Friedman and others>[Algorithm development and coordinate-descent formulation]{ref}
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