Fast Lasso-type safe screening for Fine-Gray competing risks model with ultrahigh dimensional covariates
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Publication:6629344
DOI10.1002/SIM.9545zbMATH Open1547.62502MaRDI QIDQ6629344
Zhenyuan Shen, Zhelun Tan, Hong Wang, Zhuan Zhang, Gang Li
Publication date: 29 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
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Cites Work
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- Nearly unbiased variable selection under minimax concave penalty
- The Adaptive Lasso and Its Oracle Properties
- Penalized variable selection in competing risks regression
- Elastic net for Cox’s proportional hazards model with a solution path algorithm
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- A Proportional Hazards Model for the Subdistribution of a Competing Risk
- Gap Safe screening rules for sparsity enforcing penalties
- Safe Feature Elimination in Sparse Supervised Learning
- Feature screening based on ultrahigh dimensional competing risks models
- Scalable Algorithms for Large Competing Risks Data
- Regularization and Variable Selection Via the Elastic Net
- Competing risks as a multi-state model
- On correlation rank screening for ultra-high dimensional competing risks data
- Variable selection in competing risks models based on quantile regression
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