Penalised empirical likelihood for the additive hazards model with high-dimensional data

DOI10.1080/10485252.2017.1303062zbMath1369.62057OpenAlexW2603610662MaRDI QIDQ5266567

Xuewen Lu, Jianglin Fang, Wanrong Liu

Publication date: 16 June 2017

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485252.2017.1303062

zbMATH Keywords

empirical likelihoodvariable selectionadditive hazards modelpenalised empirical likelihoodhigh-dimensional censored data

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Estimation in survival analysis and censored data (62N02)

Cites Work

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• Penalized high-dimensional empirical likelihood
• Covariate Selection for the Semiparametric Additive Risk Model
• The Dantzig Selector in Cox's Proportional Hazards Model
• Effects of data dimension on empirical likelihood
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• Empirical likelihood ratio confidence intervals for a single functional
• Semiparametric analysis of the additive risk model
• Ideal spatial adaptation by wavelet shrinkage
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• High-Dimensional Sparse Additive Hazards Regression
• Adaptive Lasso for Cox's proportional hazards model
• Variable selection for multivariate failure time data

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