Automatic Response Category Combination in Multinomial Logistic Regression

DOI10.1080/10618600.2019.1585258OpenAlexW2964195439WikidataQ128229123 ScholiaQ128229123MaRDI QIDQ3391283

Adam J. Rothman, Bradley S. Price, Charles J. Geyer

Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1705.03594

zbMATH Keywords

multinomial logistic regressionfusion penaltyresponse category reduction

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (7)

Simplex-based Multinomial Logistic Regression with Diverging Numbers of Categories and Covariates ⋮ An Efficient Convex Formulation for Reduced-Rank Linear Discriminant Analysis in High Dimensions ⋮ A Likelihood-Based Approach for Multivariate Categorical Response Regression in High Dimensions ⋮ Asymptotic properties of concave \(L_1\)-norm group penalties ⋮ Sparse group fused Lasso for model segmentation: a hybrid approach ⋮ Sparse principal component regression via singular value decomposition approach ⋮ Multiclass-penalized logistic regression

Uses Software

• glmnet
• msgl
• D-ADMM
• clusterpath

Cites Work

• Unnamed Item
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Sparse group Lasso and high dimensional multinomial classification
• Variational Analysis
• D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization
• Sparsity and Smoothness Via the Fused Lasso
• The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
• Model Selection and Estimation in Regression with Grouped Variables
• Classification of gene microarrays by penalized logistic regression

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