Proposes a semiparametric multiple imputation framework for Cox regression with diverging-dimensional missing covariates, establishing consistency and asymptotic normality of debiased pooled estimators via Rubin's rules.
Naisyin Wang and James M
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
PRADAS derives a Bayes-optimal mirror statistic for any splitting scheme, establishes asymptotic FDR control under weak dependence, and optimizes the split ratio as a stopping time to improve power over standard equal-split methods.
Formalizes support-exclusion in constrained pricing and introduces target-aware controller with certified bands and regret-information accounting that identifies when polynomial target mass succeeds but 1/t branches fail without extra movement.
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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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.