Testing a global null hypothesis using ensemble machine learning methods

DOI10.1002/sim.9362zbMATH Open1547.62258MaRDI QIDQ6628383

Ying Huang, Youyi Fong, Sunwoo Han

Publication date: 29 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

AUChypothesis testcross validationstackingrandom forestvaccine efficacy trial

Mathematics Subject Classification ID

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

Cites Work

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• Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates
• Stacked regressions
• A sharper Bonferroni procedure for multiple tests of significance
• Improved Small-Sample Estimation of Nonlinear Cross-Validated Prediction Metrics
• An Adaptive Resampling Test for Detecting the Presence of Significant Predictors
• Comment
• A Generalization of Sampling Without Replacement From a Finite Universe
• Nonparametric variable importance assessment using machine learning techniques
• The Elements of Statistical Learning
• Statistical inference for data adaptive target parameters
• A machine learning-based approach for estimating and testing associations with multivariate outcomes

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