Sparse Linear Models

Trevor Hastie, Stanford University

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