A note on path-based variable selection in the penalized proportional hazards model
From MaRDI portal
Publication:3631492
DOI10.1093/biomet/asm083zbMath1437.62681OpenAlexW2129934724MaRDI QIDQ3631492
Publication date: 10 June 2009
Published in: Biometrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1093/biomet/asm083
variable selectionoracle propertyLassopenalized partial likelihoodadaptive pathsmoothly-clipped-absolute deviation penalty
Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10)
Related Items
Oracle inequalities for the Lasso in the high-dimensional Aalen multiplicative intensity model, Model structure selection in single-index-coefficient regression models, Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review, A group bridge approach for component selection in nonparametric accelerated failure time additive regression model, Penalized generalized empirical likelihood with a diverging number of general estimating equations for censored data, Profiled adaptive elastic-net procedure for partially linear models with high-dimensional covar\-i\-ates, High-dimensional additive hazards models and the lasso, High-dimensional Cox regression analysis in genetic studies with censored survival outcomes, An ordinary differential equation-based solution path algorithm, High-Dimensional Sparse Additive Hazards Regression, Sparse estimation and inference for censored median regression, Penalized and Shrinkage Estimation in the Cox Proportional Hazards Model, On sparse estimation for semiparametric linear transformation models, Forward regression for Cox models with high-dimensional covariates, Penalized variable selection procedure for Cox models with semiparametric relative risk, Variable selection in partially linear additive hazards model with grouped covariates and a diverging number of parameters, Penalized Cox's proportional hazards model for high-dimensional survival data with grouped predictors, The Dantzig Selector in Cox's Proportional Hazards Model, Inference for non-probability samples under high-dimensional covariate-adjusted superpopulation model, A sequential feature selection procedure for high-dimensional Cox proportional hazards model, Resampling-based efficient shrinkage method for non-smooth minimands, Penalised empirical likelihood for the additive hazards model with high-dimensional data, Extended Bayesian information criterion in the Cox model with a high-dimensional feature space