Fast Marginal Likelihood Estimation of Penalties for Group-Adaptive Elastic Net.

ABSTRACT: Elastic net penalization is widely used in high-dimensional prediction and variable selection settings. Auxiliary information on the variables, for example, groups of variables, is often available. Group-adaptive elastic net penalization exploits this information to potentially improve performance by estimating group penalties, thereby penalizing important groups of variables less than other groups. Estimating these group penalties is, however, hard due to the high dimension of the data. Existing methods are computationally expensive or not generic in the type of response. Here we present a fast method for estimation of group-adaptive elastic net penalties for generalized linear models. We first derive a low-dimensional representation of the Taylor approximation of the marginal likelihood

SUBMITTER: van Nee MM 

PROVIDER: S-EPMC10511031 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature