A Likelihood-Based Approach for Multivariate Categorical Response Regression in High Dimensions

DOI10.1080/01621459.2021.1999819arXiv2007.07953MaRDI QIDQ6110028

Adam J. Rothman, Aaron J. Molstad

Publication date: 4 July 2023

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2007.07953

zbMATH Keywords

classificationconvex optimizationmultinomial logistic regressionmulti-label classificationcategorical data analysis

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Binned multinomial logistic regression for integrative cell-type annotation

Cites Work

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• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Sparse group Lasso and high dimensional multinomial classification
• Self-concordant analysis for logistic regression
• Multivariate logistic models
• MM Optimization Algorithms
• Automatic Response Category Combination in Multinomial Logistic Regression
• Multiclass Sparse Discriminant Analysis
• Nuclear penalized multinomial regression with an application to predicting at bat outcomes in baseball
• Classification of gene microarrays by penalized logistic regression
• A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers

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