Variable selection using a smooth information criterion for distributional regression models.

ABSTRACT: Modern variable selection procedures make use of penalization methods to execute simultaneous model selection and estimation. A popular method is the least absolute shrinkage and selection operator, the use of which requires selecting the value of a tuning parameter. This parameter is typically tuned by minimizing the cross-validation error or Bayesian information criterion, but this can be computationally intensive as it involves fitting an array of different models and selecting the best one. In contrast with this standard approach, we have developed a procedure based on the so-called "smooth IC" (SIC) in which the tuning parameter is automatically selected in one step. We also extend this model selection procedure to the distributional regression framework, which is more flexible than c

SUBMITTER: O'Neill M 

PROVIDER: S-EPMC10121547 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature