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Sparse Linear Models
Date: 2016/7/4             Browse: 639

Sparse Linear Models

Speaker: Trevor Hastie

Time: Jul 4, 10:00am - 11:00am.

Location: Lecture hell, Administration Building


In a statistical world faced with an explosion of data, regularization has become an important ingredient. In many problems, we have many more variables than observations, and the lasso penalty and its hybrids have become increasingly useful. This talk presents a general framework for fitting large scale regularization paths for a variety of problems. We describe the approach, and demonstrate it via examples using our R package GLMNET. We then outline a series of related problems using extensions of these ideas.


Trevor Hastie was born in South Africa in 1953. He received his university education from Rhodes University, South Africa (BS), University of Cape Town (MS), and Stanford University (Ph.D Statistics 1984).

His first employment was with the South African Medical Research Council in 1977, during which time he earned his MS from UCT. In 1979 he spent a year interning at the London School of Hygiene and Tropical Medicine, the Johnson Space Center in Houston Texas, and the Biomath department at Oxford University. He joined the Ph.D program at Stanford University in 1980. After graduating from Stanford in 1984, he returned to South Africa for a year with his earlier employer SA Medical Research Council. He returned to the USA in March 1986 and joined the statistics and data analysis research group at what was then AT&T Bell Laboratories in Murray Hill, New Jersey. After eight years at Bell Labs, he returned to Stanford University in 1994 as Professor in Statistics and Biostatistics. In 2013 he was named the John A. Overdeck Professor of Mathematical Sciences.

His main research contributions have been in applied statistics; he has published over 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 form 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.

SIST-Seminar 16052