Structured, sparse regression with application to HIV drug resistance

DOI10.1214/10-AOAS428zbMath1223.62164arXiv1002.3128OpenAlexW3105051557WikidataQ35191050 ScholiaQ35191050MaRDI QIDQ641120

Roni Rosenfeld, Daniel Percival, Kathryn Roeder, Larry Alan Wasserman

Publication date: 21 October 2011

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

Full work available at URL: https://arxiv.org/abs/1002.3128

zbMATH Keywords

regressionvariable selectiongreedy algorithmssparsity

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Linear inference, regression (62J99) Medical applications (general) (92C50)

Related Items (5)

Overlapping group lasso for high-dimensional generalized linear models ⋮ Theoretical properties of the overlapping groups Lasso ⋮ Structured, Sparse Aggregation ⋮ High-dimensional generalized linear models incorporating graphical structure among predictors ⋮ Network‐Based Penalized Regression With Application to Genomic Data

Uses Software

• glmnet
• geno2pheno

Cites Work

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• Persistene in high-dimensional linear predictor-selection and the virtue of overparametrization
• Sparsity oracle inequalities for the Lasso
• Approximation and learning by greedy algorithms
• Sparsity and Smoothness Via the Fused Lasso
• Information-Theoretic Limits on Sparsity Recovery in the High-Dimensional and Noisy Setting
• Learning with Structured Sparsity
• Model Selection and Estimation in Regression with Grouped Variables
• Semiparametric Regression of Multidimensional Genetic Pathway Data: Least‐Squares Kernel Machines and Linear Mixed Models

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