Pages that link to "Item:Q2254461"
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The following pages link to Buckley-James boosting for survival analysis with high-dimensional biomarker data (Q2254461):
Displaying 16 items.
- On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models (Q722722) (← links)
- Bi-level feature selection in high dimensional AFT models with applications to a genomic study (Q2195271) (← links)
- A gradient boosting algorithm for survival analysis via direct optimization of concordance index (Q2262348) (← links)
- Empirical Likelihood for Censored Linear Regression and Variable Selection (Q2949877) (← links)
- Boosting method for nonlinear transformation models with censored survival data (Q3304938) (← links)
- Identification of biomarker‐by‐treatment interactions in randomized clinical trials with survival outcomes and high‐dimensional spaces (Q5280185) (← links)
- An overview of techniques for linking high‐dimensional molecular data to time‐to‐event endpoints by risk prediction models (Q5391149) (← links)
- Doubly Penalized Buckley–James Method for Survival Data with High‐Dimensional Covariates (Q5450464) (← links)
- Unbiased Boosting Estimation for Censored Survival Data (Q6185138) (← links)
- A model-free machine learning method for risk classification and survival probability prediction (Q6537809) (← links)
- Regularized Buckley-James method for right-censored outcomes with block-missing multimodal covariates (Q6544014) (← links)
- A Cox-optimized survival model based on GrowNet (Q6552588) (← links)
- Buckley-James boosting model based on extreme learning machine and random survival forests (Q6563673) (← links)
- Privacy-preserving and homogeneity-pursuit integrative analysis for high-dimensional censored data (Q6579418) (← links)
- High-dimensional single-index models with censored responses (Q6627541) (← links)
- Accelerated failure time models with error-prone response and nonlinear covariates (Q6643215) (← links)
