High-dimensional variable selection accounting for heterogeneity in regression coefficients across multiple data sources

DOI10.1002/CJS.11793MaRDI QIDQ6632391

Tingting Yu, Shangyuan Ye, Rui Wang

Publication date: 4 November 2024

Published in: The Canadian Journal of Statistics (Search for Journal in Brave)

zbMATH Keywords

ADMMvariable selectiondata heterogeneity\( k\)-meanscoefficient clustering

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Title not available (Why is that?)
• Title not available (Why is that?)
• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Properties and refinements of the fused Lasso
• Least angle regression. (With discussion)
• On the adaptive elastic net with a diverging number of parameters
• Penalized Generalized Estimating Equations for High-Dimensional Longitudinal Data Analysis
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparsity and Smoothness Via the Fused Lasso
• Grouping Pursuit Through a Regularization Solution Surface
• Fused lasso with the adaptation of parameter ordering in combining multiple studies with repeated measurements
• Homogeneity Pursuit
• Individualized Multidirectional Variable Selection
• The Elements of Statistical Learning
• A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption
• Capturing Heterogeneity in repeated measures data by fusion penalty

This page was built for publication: High-dimensional variable selection accounting for heterogeneity in regression coefficients across multiple data sources

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6632391)