Estimation by polynomial splines with variable selection in additive Cox models
DOI10.1080/02331888.2012.748770zbMath1367.62132OpenAlexW2019118991MaRDI QIDQ5169752
Li-chun Wang, Heng Lian, Shang-Li Zhang
Publication date: 11 July 2014
Published in: Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331888.2012.748770
polynomial splinesAkaike information criterionBayesian information criterionproportional hazards modelsgeneralized cross-validation
Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Generalized linear models (logistic models) (62J12) Reliability and life testing (62N05)
Related Items (3)
Uses Software
Cites Work
- The Adaptive Lasso and Its Oracle Properties
- Regularization for Cox's proportional hazards model with NP-dimensionality
- Estimation and variable selection for generalized additive partial linear models
- Penalized variable selection procedure for Cox models with semiparametric relative risk
- Variable selection in nonparametric additive models
- SCAD-penalized regression in high-dimensional partially linear models
- High-dimensional additive modeling
- Local likelihood and local partial likelihood in hazard regression
- Estimating multiplicative and additive hazard functions by kernel methods
- Efficient estimation of the partly linear additive Cox model
- Variable selection for Cox's proportional hazards model and frailty model
- Variable selection in semiparametric regression modeling
- Estimation in additive Cox models by marginal integration
- Variable selection and estimation in high-dimensional varying-coefficient models
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Shrinkage Estimation of the Varying Coefficient Model
- Partially Linear Hazard Regression for Multivariate Survival Data
- Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements
- Tuning parameter selectors for the smoothly clipped absolute deviation method
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