Sparse matrix linear models for structured high-throughput data

DOI10.1214/21-AOAS1444zbMath1498.62232arXiv1712.05767WikidataQ114599236 ScholiaQ114599236MaRDI QIDQ2135347

Jane W. Liang, Śaunak Sen

Publication date: 6 May 2022

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

Full work available at URL: https://arxiv.org/abs/1712.05767

zbMATH Keywords

ADMMJuliagradient descentLassoFISTAproximal gradient algorithms

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Convex programming (90C25)

Related Items (1)

MatrixLMnet

Uses Software

• R
• fda (R)
• glmnet
• MESS
• limma
• hglasso
• Mutoss
• Julia
• selectiveInference
• R/qtl
• data.table
• GitHub
• sma

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Least angle regression. (With discussion)
• Coordinate descent algorithms for lasso penalized regression
• Functional data analysis.
• A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Julia: A Fresh Approach to Numerical Computing
• A study of error variance estimation in Lasso regression
• Another Look at the Fast Iterative Shrinkage/Thresholding Algorithm (FISTA)
• Adaptive FISTA for Nonconvex Optimization
• Regularization and Variable Selection Via the Elastic Net

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