J
Jerome H. Friedman
Researcher at Stanford University
Publications - 158
Citations - 156262
Jerome H. Friedman is an academic researcher from Stanford University. The author has contributed to research in topics: Lasso (statistics) & Multivariate statistics. The author has an hindex of 70, co-authored 155 publications receiving 138619 citations. Previous affiliations of Jerome H. Friedman include University of Washington.
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Regularized Discriminant Analysis
TL;DR: Alternatives to the usual maximum likelihood estimates for the covariance matrices are proposed, characterized by two parameters, the values of which are customized to individual situations by jointly minimizing a sample-based estimate of future misclassification risk.
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A Statistical View of Some Chemometrics Regression Tools
TL;DR: In this article, the authors examined partial least squares and principal components regression from a statistical perspective and compared them with other statistical methods intended for those situations, such as variable subset selection and ridge regression.
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Projection Pursuit Regression
TL;DR: In this article, a nonparametric multiple regression (NMM) method is presented, which models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner.
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Pathwise coordinate optimization
TL;DR: It is shown that coordinate descent is very competitive with the well-known LARS procedure in large lasso problems, can deliver a path of solutions efficiently, and can be applied to many other convex statistical problems such as the garotte and elastic net.