Pages that link to "Item:Q391867"

The following pages link to Adjusted regularized estimation in the accelerated failure time model with high dimensional covariates (Q391867):

Displaying 17 items.

• Variable selection for survival data with a class of adaptive elastic net techniques (Q294255) (← links)
• Robust estimators of accelerated failure time regression with generalized log-gamma errors (Q1658482) (← links)
• Low-dimensional confounder adjustment and high-dimensional penalized estimation for survival analysis (Q2013304) (← links)
• Sparsity-restricted estimation for the accelerated failure time model (Q2073554) (← links)
• \(\ell_0\)-regularized high-dimensional accelerated failure time model (Q2129574) (← links)
• Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models (Q2134161) (← links)
• Adaptive penalized weighted least absolute deviations estimation for the accelerated failure time model (Q2194752) (← links)
• Stability selection for Lasso, ridge and elastic net implemented with AFT models (Q2195264) (← links)
• Parameter estimation of partial linear model under monotonicity constraints with censored data (Q2515856) (← links)
• The Jackknife estimate of covariance of two Kaplan–Meier integrals with covariables (Q3462135) (← links)
• Regularized Estimation for the Accelerated Failure Time Model (Q3636981) (← links)
• Covariate selection for accelerated failure time data (Q4976275) (← links)
• Inference for Low-Dimensional Covariates in a High-Dimensional Accelerated Failure Time Model (Q5226613) (← links)
• Weighted least squares model averaging for accelerated failure time models (Q6115536) (← links)
• -Penalized Pairwise Difference Estimation for a High-Dimensional Censored Regression Model (Q6190327) (← links)
• Reproducible learning for accelerated failure time models via deep knockoffs (Q6588680) (← links)
• Rank-based sequential feature selection for high-dimensional accelerated failure time models with main and interaction effects (Q6626705) (← links)