Penalized variable selection in competing risks regression

DOI10.1007/s10985-016-9362-3zbMath1402.62164OpenAlexW2331759487WikidataQ39887514 ScholiaQ39887514MaRDI QIDQ2364037

Chirag R. Parikh, Zhixuan Fu, Bingqing Zhou

Publication date: 17 July 2017

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10985-016-9362-3

zbMATH Keywords

parameter estimationvariable selectioncompeting riskscumulative incidence functiongroup variable selectionoracle propertiesproportional subdistribution hazardpenalized variable selection

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Estimation in survival analysis and censored data (62N02)

Related Items (6)

Scalable Algorithms for Large Competing Risks Data ⋮ Joint model-free feature screening for ultra-high dimensional semi-competing risks data ⋮ The LASSO on latent indices for regression modeling with ordinal categorical predictors ⋮ Group and within-group variable selection for competing risks data ⋮ Non-marginal feature screening for varying coefficient competing risks model ⋮ On correlation rank screening for ultra-high dimensional competing risks data

Uses Software

• glmnet
• cmprsk
• ISLR

Cites Work

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