An ADMM with continuation algorithm for non-convex SICA-penalized regression in high dimensions

DOI10.1080/00949655.2018.1448397OpenAlexW2792312242MaRDI QIDQ4960646

Deyi Xu, Yue-Yong Shi, Yuan Shan Wu, Yu Ling Jiao

Publication date: 23 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2018.1448397

zbMATH Keywords

ADMMcontinuationcoordinate descenthigh-dimensional BICSICA

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (3)

A primal dual active set with continuation algorithm for high-dimensional nonconvex SICA-penalized regression ⋮ Unnamed Item ⋮ Variable selection via generalized SELO-penalized Cox regression models

Uses Software

• flare
• sparsenet
• QICD

Cites Work

• Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
• The group exponential lasso for bi-level variable selection
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Nearly unbiased variable selection under minimax concave penalty
• A unified approach to model selection and sparse recovery using regularized least squares
• The Adaptive Lasso and Its Oracle Properties
• An alternating direction method with continuation for nonconvex low rank minimization
• SICA for Cox's proportional hazards model with a diverging number of parameters
• Statistics for high-dimensional data. Methods, theory and applications.
• A primal dual active set with continuation algorithm for the \(\ell^0\)-regularized optimization problem
• One-step sparse estimates in nonconcave penalized likelihood models
• An alternating direction method for finding Dantzig selectors
• Alternating direction method for covariance selection models
• Calibrating nonconvex penalized regression in ultra-high dimension
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
• Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
• SparseNet: Coordinate Descent With Nonconvex Penalties
• Alternating direction methods for classical and ptychographic phase retrieval
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• A Primal Dual Active Set Algorithm With Continuation for Compressed Sensing
• High-Dimensional Sparse Additive Hazards Regression
• Efficient Algorithms for Robust Recovery of Images From Compressed Data
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• The Mnet method for variable selection
• Convergence of a block coordinate descent method for nondifferentiable minimization
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: An ADMM with continuation algorithm for non-convex SICA-penalized regression in high dimensions