Minimization of Akaike's information criterion in linear regression analysis via mixed integer nonlinear program

DOI10.1080/10556788.2017.1333611zbMath1398.90111arXiv1606.05030OpenAlexW3098332525MaRDI QIDQ4637833

Keiji Kimura, Hayato Waki

Publication date: 3 May 2018

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1606.05030

zbMATH Keywords

branch-and-boundmixed integer nonlinear programSCIP and Akaike's information criterion

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Mixed integer programming (90C11) Polyhedral combinatorics, branch-and-bound, branch-and-cut (90C57)

Related Items

Mixed integer quadratic optimization formulations for eliminating multicollinearity based on variance inflation factor, Best subset selection via cross-validation criterion, Unnamed Item, An efficient optimization approach for best subset selection in linear regression, with application to model selection and fitting in autoregressive time-series, A Mixed-Integer Fractional Optimization Approach to Best Subset Selection

Uses Software

• LAPACK
• R
• SCIP
• UG
• UCI-ml
• CPLEX
• ParaSCIP
• MAIC

Cites Work

• Unnamed Item
• Best subset selection via a modern optimization lens
• Mixed integer second-order cone programming formulations for variable selection in linear regression
• SCIP: solving constraint integer programs
• Algorithm for cardinality-constrained quadratic optimization
• Mixed Integer Nonlinear Program for Minimization of Akaike’s Information Criterion
• LAPACK Users' Guide
• Constraint Pairing In Integer Programming
• SCIP: global optimization of mixed-integer nonlinear programs in a branch-and-cut framework
• 10.1162/153244303322753616
• A new look at the statistical model identification