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{{Hastie Tibshirani Friedman The Elements of Statistical Learning 2009}} will display: Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome H. (February 2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (pdf). Graduate Texts in Statistics (2nd ed.). New York: Springer-Verlag. ISBN 978-0-387-84857-0. OCLC 405547558.
Hastie is a prolific author of scientific works on various topics in applied statistics, including statistical learning, data mining, statistical computing, and bioinformatics. He along with his collaborators has authored about 125 scientific articles.
In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani. [1] Suppose we expect a response variable to be determined by a linear combination of a subset of potential covariates.
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Tibshirani was made the 2012 Statistical Society of Canada's Gold Medalist at their yearly meeting in Guelph, Ontario for "exceptional contributions to methodology and theory for the analysis of complex data sets, smoothing and regression methodology, statistical learning, and classification, and application areas that include public health ...
GAMs were originally developed by Trevor Hastie and Robert Tibshirani [1] to blend properties of generalized linear models with additive models. They can be interpreted as the discriminative generalization of the naive Bayes generative model. [2] The model relates a univariate response variable, Y, to some predictor variables, x i.
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In machine learning and computational learning theory, LogitBoost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani.. The original paper casts the AdaBoost algorithm into a statistical framework. [1]