Low-dimensional confounder adjustment and high-dimensional penalized estimation for survival analysis

DOI10.1007/S10985-015-9350-ZzbMath1372.62089OpenAlexW1926250569WikidataQ40435695 ScholiaQ40435695MaRDI QIDQ2013304

Xiaochao Xia, Jia-Liang Li, Wenyang Zhang, Binyan Jiang

Publication date: 17 August 2017

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

Full work available at URL: https://doi.org/10.1007/s10985-015-9350-z

zbMATH Keywords

survival analysisvariable selectiongene expressionaccelerated failure time modelpenalized estimationconfounder adjustmentindependent screening

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Estimation in survival analysis and censored data (62N02)

Related Items (8)

Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients ⋮ Conditional sparse boosting for high-dimensional instrumental variable estimation ⋮ Copula-based Partial Correlation Screening: a Joint and Robust Approach ⋮ Feature Screening with Latent Responses ⋮ Estimation in additive models with fixed censored responses ⋮ Multi-threshold accelerated failure time model ⋮ Sure independence screening in ultrahigh dimensional generalized additive models ⋮ Penalized empirical likelihood for the sparse Cox regression model

Cites Work

• SCAD-penalized regression in high-dimensional partially linear models
• On the definition of a confounder
• Consistent estimation under random censorship when covariables are present
• SCAD-penalized regression in additive partially linear proportional hazards models with an ultra-high-dimensional linear part
• Regularized Estimation in the Accelerated Failure Time Model with High-Dimensional Covariates
• Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models
• Regularized Estimation for the Accelerated Failure Time Model
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Statistical Estimation in Generalized Multiparameter Likelihood Models

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