Pages that link to "Item:Q3168373"
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The following pages link to Analysis of Survival Data with Group Lasso (Q3168373):
Displaying 17 items.
- Doubly regularized Cox regression for high-dimensional survival data with group structures (Q897169) (← links)
- A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds (Q2057739) (← links)
- Penalized Cox's proportional hazards model for high-dimensional survival data with grouped predictors (Q2058906) (← links)
- Regression models for grouped survival data: estimation and sensitivity analysis (Q2445808) (← links)
- Nonidentical twins: comparison of frequentist and Bayesian Lasso for Cox models (Q2803426) (← links)
- Modeling Grouped Survival Data with Time-Dependent Covariates (Q3424304) (← links)
- Identification of homogeneous and heterogeneous variables in pooled cohort studies (Q3459937) (← links)
- Group variable selection via convex log‐exp‐sum penalty with application to a breast cancer survivor study (Q3465722) (← links)
- Hierarchically penalized Cox regression with grouped variables (Q3633156) (← links)
- The change-plane Cox model (Q4562737) (← links)
- (Q4844393) (← links)
- Variable selection with group LASSO approach: Application to Cox regression with frailty model (Q5082578) (← links)
- Counterfactual Explanation of Machine Learning Survival Models (Q5862149) (← links)
- Group variable selection for the Cox model with interval-censored failure time data (Q6589252) (← links)
- Variable selection in binary logistic regression for modelling bankruptcy risk (Q6615795) (← links)
- Structured learning in time-dependent Cox models (Q6618308) (← links)
- A network-constrain Weibull AFT model for biomarkers discovery (Q6649357) (← links)
