Hierarchical sparse modeling: a choice of two group Lasso formulations

DOI10.1214/17-STS622zbMath1442.62162arXiv1512.01631OpenAlexW2558723476MaRDI QIDQ1704702

Xiaohan Yan, Jacob Bien

Publication date: 13 March 2018

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1512.01631

zbMATH Keywords

group Lassohierarchical sparsityconvex regularizationlatent overlapping group Lasso

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (11)

A Pliable Lasso ⋮ A First-Order Optimization Algorithm for Statistical Learning with Hierarchical Sparsity Structure ⋮ The LASSO on latent indices for regression modeling with ordinal categorical predictors ⋮ Unnamed Item ⋮ A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data ⋮ A dynamic screening algorithm for hierarchical binary marketing data ⋮ Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages ⋮ A two-stage regularization method for variable selection and forecasting in high-order interaction model ⋮ Reluctant generalized additive modeling ⋮ Graphical-model based high dimensional generalized linear models ⋮ Unnamed Item

Uses Software

• glinternet
• gamsel

Cites Work

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• Variable Selection Using Adaptive Nonlinear Interaction Structures in High Dimensions
• Model Selection and Estimation in Regression with Grouped Variables
• Convergence of a block coordinate descent method for nondifferentiable minimization
• Structured sparsity through convex optimization

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