Greedy Variable Selection for High-Dimensional Cox Models
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Publication:6069891
DOI10.5705/ss.202021.0265MaRDI QIDQ6069891
Ching-Kang Ing, Yu-Jen Cheng, Chien-Tong Lin
Publication date: 17 November 2023
Published in: Statistica Sinica (Search for Journal in Brave)
variable selection consistencysure screeninghigh-dimensional information criterionChebyshev greedy algorithm
Cites Work
- A stepwise regression method and consistent model selection for high-dimensional sparse linear models
- Regularization for Cox's proportional hazards model with NP-dimensionality
- Conditional screening for ultra-high dimensional covariates with survival outcomes
- Variable selection for Cox's proportional hazards model and frailty model
- Restricted strong convexity implies weak submodularity
- Model selection for high-dimensional linear regression with dependent observations
- Forward regression for Cox models with high-dimensional covariates
- Greedy approximation in convex optimization
- Extended Bayesian information criterion in the Cox model with a high-dimensional feature space
- Boosting for high-dimensional linear models
- Censored rank independence screening for high-dimensional survival data
- Forward Regression for Ultra-High Dimensional Variable Screening
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
- Non-asymptotic oracle inequalities for the high-dimensional cox regression via lasso
- Adaptive Lasso for Cox's proportional hazards model
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