|
Abstract:
|
In many engineering and scientific applications, predictor variables are grouped, for example, in biological applications where assayed genes or proteins can be grouped by biological roles or biological pathways. Existing successful group variable selection methods have the limitation of selecting variables in an "all-in-all-out" fashion (i.e., when one variable in a group is selected) all other variables in the same group are also selected. In this paper, we develop a new group variable selection method that not only removes unimportant groups effectively, but also keeps the flexibility of selecting variables within a group. We also show that the new method offers the potential for achieving the theoretical "oracle" property for both cases where the number of predictors is fixed and the number of groups diverges.
|
