A neutral comparison of algorithms to minimize \(L_0\) penalties for high-dimensional variable selection

DOI10.1002/BIMJ.202200207zbMATH Open1547.6223MaRDI QIDQ6625366

Florian Frommlet

Publication date: 28 October 2024

Published in: Biometrical Journal (Search for Journal in Brave)

zbMATH Keywords

high-dimensional datavariable selectionneutral comparison\(L_0\) penalties

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Title not available (Why is that?)
• Title not available (Why is that?)
• To explain or to predict?
• Estimating the dimension of a model
• False discoveries occur early on the Lasso path
• The risk inflation criterion for multiple regression
• GSDAR: a fast Newton algorithm for \(\ell_0\) regularized generalized linear models with statistical guarantee
• Sparse regression: scalable algorithms and empirical performance
• Adaptive robust variable selection
• Phenotypes and genotypes. The search for influential genes
• Extending the Modified Bayesian Information Criterion (mBIC) to Dense Markers and Multiple Interval Mapping
• Variable selection – A review and recommendations for the practicing statistician
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Penalized Composite Quasi-Likelihood for Ultrahigh Dimensional Variable Selection
• A Mixed-Integer Fractional Optimization Approach to Best Subset Selection
• Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
• A new look at the statistical model identification

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