Seminar
Peter Radchenko
Variable Inclusion and Shrinkage Algorithms
The Lasso is a popular and computationally efficient procedure for automatically performing both
variable selection and coefficient shrinkage on linear regression models. One limitation of the
Lasso is that the same tuning parameter is used for both variable selection and shrinkage. As a result,
it may end up selecting a model with too many variables to prevent over shrinkage of the regression
coefficients. We will discuss a new class of methods called Variable Inclusion and Shrinkage Algorithms
(VISA). This approach is capable of selecting sparse models while avoiding over shrinkage problems.
VISA uses a path algorithm, so it is computationally efficient. It will be shown through extensive
simulations that the new approach significantly outperforms the Lasso and also provides improvements over
more recent procedures, such as the Dantzig selector, Relaxed Lasso and Adaptive Lasso. 