Mixed integer second-order cone programming formulations for variable selection in linear regression
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Publication:320071
DOI10.1016/j.ejor.2015.06.081zbMath1346.90616OpenAlexW1064564656MaRDI QIDQ320071
Ryuhei Miyashiro, Yuichi Takano
Publication date: 6 October 2016
Published in: European Journal of Operational Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ejor.2015.06.081
integer programmingvariable selectioninformation criterionmultiple linear regressionsecond-order cone programming
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- Using simulated annealing to optimize the feature selection problem in marketing applications
- Choosing the best set of variables in regression analysis using integer programming
- Algorithm for cardinality-constrained quadratic optimization
- Multi-step methods for choosing the best set of variables in regression analysis
- Efficient algorithms for computing the best subset regression models for large-scale problems
- Applications of second-order cone programming
- Selection of relevant features and examples in machine learning
- Wrappers for feature subset selection
- Estimating the dimension of a model
- Robust classification and regression using support vector machines
- An efficient support vector machine learning method with second-order cone programming for large-scale problems
- Regressions by Leaps and Bounds
- A Biometrics Invited Paper. The Analysis and Selection of Variables in Linear Regression
- Further analysis of the data by Akaike's information criterion and the finite corrections
- A New Formula for Predicting the Shrinkage of the Coefficient of Multiple Correlation
- Model Selection and Multimodel Inference
- Regression Model Selection—A Residual Likelihood Approach
- 10.1162/153244303322753616
- Dimension Reduction and Coefficient Estimation in Multivariate Linear Regression
- Computational Methods of Feature Selection
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- A new look at the statistical model identification