Graph structured sparse subset selection
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Publication:2662712
DOI10.1016/j.ins.2019.12.086zbMath1462.62364OpenAlexW2996774258WikidataQ126412702 ScholiaQ126412702MaRDI QIDQ2662712
Hyungrok Do, Seoung Bum Kim, Myun Seok Cheon
Publication date: 14 April 2021
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2019.12.086
Linear regression; mixed models (62J05) Statistical ranking and selection procedures (62F07) Probabilistic graphical models (62H22)
Uses Software
Cites Work
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- Nearly unbiased variable selection under minimax concave penalty
- Best subset selection via a modern optimization lens
- Mixed integer second-order cone programming formulations for variable selection in linear regression
- Variable selection and regression analysis for graph-structured covariates with an application to genomics
- The sparse Laplacian shrinkage estimator for high-dimensional regression
- Shrinkage and model selection with correlated variables via weighted fusion
- The composite absolute penalties family for grouped and hierarchical variable selection
- Penalized regression combining the \( L_{1}\) norm and a correlation based penalty
- Global discriminative-based nonnegative spectral clustering
- Network‐Based Penalized Regression With Application to Genomic Data
- Simultaneous supervised clustering and feature selection over a graph
- Incorporating Predictor Network in Penalized Regression with Application to Microarray Data
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- BEST SUBSET SELECTION FOR ELIMINATING MULTICOLLINEARITY
- Sparsity and Smoothness Via the Fused Lasso
- Likelihood-Based Selection and Sharp Parameter Estimation
- Regularization and Variable Selection Via the Elastic Net
- The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization
- Model Selection and Estimation in Regression with Grouped Variables
- Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR
- The Mnet method for variable selection
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