Regularization: Model Selection and Lasso

Wednesday, June 19, 2013 - 9:00am - 10:30am
Lind 305
Bin Yu (University of California, Berkeley)
LS and Maximum Likelihood estimation (MLE) overfit when the dimension of
the model is not small relative to the sample size. This happens almost
always in high-dimensions. Regularziation often works by adding a penalty
to the fitting criterion as in classical model selection methods such as
AIC or BIC and L1-penalized LS called Lasso. We will also introduce Cross-validation (CV) for regularization parameter selection.