scientific article; zbMATH DE number 7387624

DOI10.11329/jjssj.50.343MaRDI QIDQ5011563

Yuichi Takano, Ryuhei Miyashiro

Publication date: 27 August 2021

Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• CPLEX
• Gurobi
• Optimal-SPCA

Cites Work

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• Best subset selection via a modern optimization lens
• Feature subset selection for logistic regression via mixed integer optimization
• Mixed integer second-order cone programming formulations for variable selection in linear regression
• Feature selection for support vector machines via mixed integer linear programming
• Supersparse linear integer models for optimized medical scoring systems
• Choosing the best set of variables in regression analysis using integer programming
• Applications of second-order cone programming
• Estimating the dimension of a model
• Second-order cone programming
• Logistic regression: from art to science
• A brief history of linear and mixed-integer programming computation
• Subset selection for multiple linear regression via optimization
• Best subset selection via cross-validation criterion
• Sparse regression: scalable algorithms and empirical performance
• Best subset, forward stepwise or Lasso? Analysis and recommendations based on extensive comparisons
• Certifiably optimal sparse principal component analysis
• An overview of bilevel optimization
• Mixed integer quadratic optimization formulations for eliminating multicollinearity based on variance inflation factor
• OR Forum—An Algorithmic Approach to Linear Regression
• Regressions by Leaps and Bounds
• A Biometrics Invited Paper. The Analysis and Selection of Variables in Linear Regression
• BEST SUBSET SELECTION FOR ELIMINATING MULTICOLLINEARITY
• 10.1162/153244303322753616
• PIECEWISE-LINEAR APPROXIMATION FOR FEATURE SUBSET SELECTION IN A SEQUENTIAL LOGIT MODEL
• Regularization and Variable Selection Via the Elastic Net
• Computational Methods of Feature Selection
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Some Comments on C P
• Optimal classification trees
• A new look at the statistical model identification

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