Multiple hypothesis testing for variable selection

DOI10.1111/anzs.12157MaRDI QIDQ6569948

Florian Rohart

Publication date: 9 July 2024

Published in: Australian \& New Zealand Journal of Statistics (Search for Journal in Brave)

zbMATH Keywords

linear modelsupport estimationhigh-dimension

Mathematics Subject Classification ID

Linear inference, regression (62Jxx) Multivariate analysis (62Hxx) Nonparametric inference (62Gxx)

Cites Work

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• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Some theoretical results on the grouped variables Lasso
• High-dimensional variable selection
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• Sparsity oracle inequalities for the Lasso
• High-dimensional graphs and variable selection with the Lasso
• Consistent variable selection in high dimensional regression via multiple testing
• A general framework for multiple testing dependence
• A Factor Model Approach to Multiple Testing Under Dependence
• Forward Regression for Ultra-High Dimensional Variable Screening
• Extended Bayesian information criteria for model selection with large model spaces
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Stability Selection
• Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
• Correlation and Large-Scale Simultaneous Significance Testing
• Regularization and Variable Selection Via the Elastic Net
• On model selection curves

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