Structured sparse logistic regression with application to lung cancer prediction using breath volatile biomarkers

DOI10.1002/SIM.8454zbMATH Open1546.62911MaRDI QIDQ6627506

Qingzhao Zhang, Xiaochen Zhang, Shuangge Ma, Xiaofeng Wang, Kuangnan Fang

Publication date: 29 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

high-dimensional datavariable selectiontime-dependent variablesgroup smooth-penaltygroup spline-penalty

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• The sparse Laplacian shrinkage estimator for high-dimensional regression
• The smooth-Lasso and other \(\ell _{1}+\ell _{2}\)-penalized methods
• Adaptive fused LASSO in grouped quantile regression
• The Group Lasso for Logistic Regression
• A group bridge approach for variable selection
• Sparsity and Smoothness Via the Fused Lasso
• Iterative Solution of Nonlinear Equations in Several Variables
• Label‐noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study
• Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization
• Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements
• Model Selection and Estimation in Regression with Grouped Variables
• Convergence of a block coordinate descent method for nondifferentiable minimization
• Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
• A selective review of group selection in high-dimensional models

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