A generalization of the Benjamini-Hochberg procedure controls the FDR curve below any specified level in location families, and the standard procedure simultaneously controls the entire curve for free.
The Annals of Statistics , volume =
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
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2026 5verdicts
UNVERDICTED 5representative citing papers
Develops two methods for inference on unit-specific coefficients in latent-group panel data models that incorporate uncertainty in group assignments to gain efficiency over unit-by-unit approaches.
Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.
A formula approximating degrees of freedom for tree-structured varying coefficient models is proposed to improve BIC model selection over naive parameter counting.
Simulations show Ridge, Lasso, and ElasticNet perform similarly for prediction at high sample-to-feature ratios, but Lasso feature selection recall drops to 0.18 under high multicollinearity and low SNR while ElasticNet holds at 0.93.
citing papers explorer
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Simultaneous false discovery rate control in location families
A generalization of the Benjamini-Hochberg procedure controls the FDR curve below any specified level in location families, and the standard procedure simultaneously controls the entire curve for free.
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Inference methods for unit-specific coefficients in panel data models with latent group structure
Develops two methods for inference on unit-specific coefficients in latent-group panel data models that incorporate uncertainty in group assignments to gain efficiency over unit-by-unit approaches.
-
Constrained Weighted Bayesian Bootstrap
Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.
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A tool to determine the degrees of freedom in tree-structured varying coefficient models
A formula approximating degrees of freedom for tree-structured varying coefficient models is proposed to improve BIC model selection over naive parameter counting.
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Choosing the Right Regularizer for Applied ML: Simulation Benchmarks of Popular Scikit-learn Regularization Frameworks
Simulations show Ridge, Lasso, and ElasticNet perform similarly for prediction at high sample-to-feature ratios, but Lasso feature selection recall drops to 0.18 under high multicollinearity and low SNR while ElasticNet holds at 0.93.