Event Date:
Wednesday, May 22, 2019 - 3:30pm to 4:30pm
Event Location:
- ESB 1001 (Engineering Science Building)
Title: Statistical Learning with Sparsity
Abstract:
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.
Bio:
Trevor Hastie's main research contributions have been in the field of applied nonparametric regression and classification, and he has written two books in this area: "Generalized Additive Models" (with R. Tibshirani, Chapman and Hall, 1991), and "Elements of Statistical Learning" (with R. Tibshirani and J. Friedman, Springer 2001). He has also made contributions in statistical computing, co-editing (with J. Chambers) a large software library on modeling tools in the S-plus language ("Statistical Models in S", Wadsworth, 1992).
May 7, 2019 - 3:35pm