Automated subset selection via information criteria optimization in generalized linear models

Benjamin Schwendinger (), Florian Schwendinger () and Laura Vana-Gür ()
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Benjamin Schwendinger: TU Wien
Florian Schwendinger: University of Klagenfurt
Laura Vana-Gür: TU Wien

Computational Statistics, 2025, vol. 40, issue 9, No 32, 5831 pages

Abstract: 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.

Keywords: Constrained Optimization; Generalized Linear Models; Information Criteria; Mixed-Integer Conic Optimization; Subset Selection (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-025-01672-9

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