Learning Coefficient Heterogeneity over Networks: A Distributed Spanning-Tree-Based Fused-Lasso Regression

DOI10.1080/01621459.2022.2126363OpenAlexW4297983258MaRDI QIDQ6154008

No author found.

Publication date: 19 March 2024

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2022.2126363

zbMATH Keywords

minimum spanning treeMODISadaptive fused-lassodistributed generalized ADMMsensornets

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• The Adaptive Lasso and Its Oracle Properties
• Spectral clustering and the high-dimensional stochastic blockmodel
• On the global and linear convergence of the generalized alternating direction method of multipliers
• On the Convergence of Decentralized Gradient Descent
• A Distributed Algorithm for Minimum-Weight Spanning Trees
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Distributed Sparse Linear Regression
• On the Linear Convergence of the ADMM in Decentralized Consensus Optimization
• Decentralized Quasi-Newton Methods
• Distributed Subgradient Methods for Multi-Agent Optimization
• Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks
• Spatial Homogeneity Pursuit of Regression Coefficients for Large Datasets
• Homogeneity Pursuit
• Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model

This page was built for publication: Learning Coefficient Heterogeneity over Networks: A Distributed Spanning-Tree-Based Fused-Lasso Regression