318 Campus Drive
Stanford, CA 94305
USA
Statistical Learning with Big Data
Presenter: Trevor Hastie, PhD
Professor of Statistics and Biostatistics
Stanford University
Trevor Hastie’s main research contributions have been in applied statistics; he has published more than 180 articles and written four books in this area: Generalized Additive Models (with R. Tibshirani; Chapman and Hall, 1991), Elements of Statistical Learning (with R. Tibshirani and J. Friedman; Springer, 2001; second edition, 2009), An Introduction to Statistical Learning, with Applications in R (with G. James, D. Witten, and R. Tibshirani; Springer, 2013), and Statistical Learning with Sparsity (with R. Tibshirani and M. Wainwright; Chapman and Hall, 2015). He has also made contributions in statistical computing, co-editing (with J. Chambers) a large software library on modeling tools in the S language (Statistical Models in S; Wadsworth, 1992), which forms the foundation for much of the statistical modeling in R. His current research focuses on applied statistical modeling and prediction problems in biology and genomics, medicine and industry.
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