Work

Automated subset selection via information criteria optimization in generalized linear models

Year: 2025

Type: article

Abstract: In this paper, we show how mixed-integer conic programming can be used to directly optimize information criteria such as AIC and BIC in order to automate the model selection process for a collection of GLMs. Moreover, we propose to enhance the optimization problem with a novel linear constraint that limits pairwise correlation between the selected features and is well suited to tackle pairwise multicollinearity. Through a simulation study, we show that the proposed approach achieves high accuracy in selecting the active coefficients and outperforms naive enumeration methods aimed at subset selection.

Source: Computational Statistics

DOI: 10.1007/s00180-025-01672-9

Authors: Benjamin Schwendinger, Florian Schwendinger, Laura Vana-Gür

Institutions: TU Wien, Fraunhofer Austria, University of Klagenfurt

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

Funders: Austrian Science Fund, Technische Universität Wien