A modified multinomial baseline logit model with logit functions having different covariates

DOI10.1080/03610918.2018.1529238zbMath1489.62345OpenAlexW2912394473MaRDI QIDQ5083903

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Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2018.1529238

zbMATH Keywords

multinomial logistic regressionlogit functionmultinomial baseline logit modelmultinomial sparse group Lassonominal polychotomous data

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Uses Software

• glasso
• glmnet
• aplore3
• msgl

Cites Work

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• Statistics for high-dimensional data. Methods, theory and applications.
• A coordinate gradient descent method for nonsmooth separable minimization
• Sparse group Lasso and high dimensional multinomial classification
• Variable selection in general multinomial logit models
• Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
• The Group Lasso for Logistic Regression
• Determination of the best significance level in forward stepwise logistic regression
• Applied Logistic Regression
• Convergence of a block coordinate descent method for nondifferentiable minimization

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