Pages that link to "Item:Q3633156"

The following pages link to Hierarchically penalized Cox regression with grouped variables (Q3633156):

Displaying 35 items.

• Regularization for Cox's proportional hazards model with NP-dimensionality (Q449987) (← links)
• Doubly regularized Cox regression for high-dimensional survival data with group structures (Q897169) (← links)
• Hierarchically penalized quantile regression with multiple responses (Q1622121) (← links)
• Screening group variables in the proportional hazards model (Q1650285) (← links)
• Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits (Q1659500) (← links)
• Sparse integrative clustering of multiple omics data sets (Q1951531) (← links)
• Sparse supervised dimension reduction in high dimensional classification (Q1952086) (← links)
• Variable selection in partially linear additive hazards model with grouped covariates and a diverging number of parameters (Q2032189) (← links)
• Penalized Cox's proportional hazards model for high-dimensional survival data with grouped predictors (Q2058906) (← links)
• Oracle inequalities for weighted group Lasso in high-dimensional misspecified Cox models (Q2069598) (← links)
• Hierarchically penalized additive hazards model with diverging number of parameters (Q2254832) (← links)
• Adaptive group bridge selection in the semiparametric accelerated failure time model (Q2293393) (← links)
• Group variable selection in the Andersen-Gill model for recurrent event data (Q2301106) (← links)
• Structured estimation for the nonparametric Cox model (Q2340869) (← links)
• Variable selection and structure identification for varying coefficient Cox models (Q2404416) (← links)
• Variable selection in Cox regression models with varying coefficients (Q2437864) (← links)
• Bi-level variable selection in semiparametric transformation models with right-censored data (Q2666992) (← links)
• Adaptive bi-level variable selection for multivariate failure time model with a diverging number of covariates (Q2677126) (← links)
• Penalized Gaussian process regression and classification for high-dimensional nonlinear data (Q2893384) (← links)
• Prediction-Based Structured Variable Selection through the Receiver Operating Characteristic Curves (Q3100791) (← links)
• Analysis of Survival Data with Group Lasso (Q3168373) (← links)
• Group selection in the Cox model with a diverging number of covariates (Q3195175) (← links)
• Group variable selection via convex log‐exp‐sum penalty with application to a breast cancer survivor study (Q3465722) (← links)
• (Q4674207) (← links)
• Survival prediction and variable selection with simultaneous shrinkage and grouping priors (Q4969996) (← links)
• Variable selection with group LASSO approach: Application to Cox regression with frailty model (Q5082578) (← links)
• Sparse group variable selection based on quantile hierarchical Lasso (Q5128673) (← links)
• Hierarchically penalized quantile regression (Q5222337) (← links)
• Variable selection for frailty transformation models with application to diabetic complications (Q5507364) (← links)
• Bi-level variable selection in semiparametric transformation mixture cure models for right-censored data (Q6073556) (← links)
• Kernel Ordinary Differential Equations (Q6110694) (← links)
• A Bayesian group selection with compositional responses for analysis of radiologic tumor proportions and their genomic determinants (Q6138618) (← links)
• A nonparametric method for classification trees using grouped covariates (Q6550298) (← links)
• Structured learning in time-dependent Cox models (Q6618308) (← links)
• A network-constrain Weibull AFT model for biomarkers discovery (Q6649357) (← links)