Monday, June 27, 2016 - 12:30
Trevor Hastie, Professor, Department of Statistics Professor, Department of Health, Research and Policy Stanford University
Room 1013, Earth Sciences Building (2207 Main Mall)
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.
*joint work with Jerome Friedman, Rob Tibshirani and Noah Simon.