Choosing the best set of variables in regression analysis using integer programming

DOI10.1007/s10898-008-9323-9zbMath1178.62069OpenAlexW2114180214MaRDI QIDQ839047

Hiroshi Konno, Rei Yamamoto

Publication date: 1 September 2009

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-008-9323-9

zbMATH Keywords

least absolute deviationvariable selectioncardinality constraint0-1 integer programming

Mathematics Subject Classification ID

Related Items (16)

Feature subset selection for logistic regression via mixed integer optimization ⋮ Classification of companies using maximal margin ellipsoidal surfaces ⋮ Mixed integer quadratic optimization formulations for eliminating multicollinearity based on variance inflation factor ⋮ Mixed integer second-order cone programming formulations for variable selection in linear regression ⋮ A maximal predictability portfolio using absolute deviation reformulation ⋮ Subset selection for multiple linear regression via optimization ⋮ Compositional knockoff filter for high‐dimensional regression analysis of microbiome data ⋮ Ellipsoidal buffered area under the curve maximization model with variable selection in credit risk estimation ⋮ Best subset selection via cross-validation criterion ⋮ Outer-product-free sets for polynomial optimization and oracle-based cuts ⋮ Unnamed Item ⋮ A MAXIMAL PREDICTABILITY PORTFOLIO SUBJECT TO A TURNOVER CONSTRAINT ⋮ A MAXIMAL PREDICTABILITY PORTFOLIO USING DYNAMIC FACTOR SELECTION STRATEGY ⋮ Multi-step methods for choosing the best set of variables in regression analysis ⋮ Investigating consumers' store-choice behavior via hierarchical variable selection ⋮ Optimization for L₁-Norm Error Fitting via Data Aggregation

Uses Software

• S-PLUS

Cites Work

• Credit risk assessment using statistical and machine learning: Basic methodology and risk modeling applications
• Estimation of failure probability using semi-definite logit model
• Data mining in biomedicine
• Regressions by Leaps and Bounds
• Model Selection and Multimodel Inference
• A new look at the statistical model identification
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