High-dimensional feature selection in competing risks modeling: a stable approach using a split-and-merge ensemble algorithm

DOI10.1002/BIMJ.202100164zbMATH Open1539.62331MaRDI QIDQ6550301

Han Sun, Xiaofeng Wang

Publication date: 5 June 2024

Published in: Biometrical Journal (Search for Journal in Brave)

zbMATH Keywords

variable selectioncompeting riskshigh dimensionalleast squares approximationgenomic

Mathematics Subject Classification ID

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

Cites Work

• Nearly unbiased variable selection under minimax concave penalty
• Competing Risks Quantile Regression
• Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis
• Random lasso
• Penalized variable selection procedure for Cox models with semiparametric relative risk
• Variable selection for Cox's proportional hazards model and frailty model
• Penalized variable selection in competing risks regression
• Practical methods for competing risks data: a review
• Analyzing large datasets with bootstrap penalization
• Unified LASSO Estimation by Least Squares Approximation
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• A Proportional Hazards Model for the Subdistribution of a Competing Risk
• Stability Selection
• Consistent Estimation of the Expected Brier Score in General Survival Models with Right‐Censored Event Times
• Regularization and Variable Selection Via the Elastic Net

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