Pairwise fusion approach incorporating prior constraint information

DOI10.1007/S40304-018-0168-3zbMath1441.62181OpenAlexW2950902041WikidataQ127635098 ScholiaQ127635098MaRDI QIDQ1988277

Baisuo Jin, Yaguang Li

Publication date: 16 April 2020

Published in: Communications in Mathematics and Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s40304-018-0168-3

zbMATH Keywords

homogeneitylinear regressionsparsityalternating direction method of multipliersprior constraints

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Applications of statistics to environmental and related topics (62P12) Point estimation (62F10)

Cites Work

• Unnamed Item
• Unnamed Item
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Estimating Optimal Transformations for Multiple Regression and Correlation
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparsity and Smoothness Via the Fused Lasso
• Simultaneous Grouping Pursuit and Feature Selection Over an Undirected Graph
• Homogeneity Pursuit
• Model Selection and Estimation in Regression with Grouped Variables
• Tuning Parameter Selection in High Dimensional Penalized Likelihood
• Convergence of a block coordinate descent method for nondifferentiable minimization

This page was built for publication: Pairwise fusion approach incorporating prior constraint information