Variable selection in general multinomial logit models

DOI10.1016/j.csda.2014.09.009OpenAlexW1976965124MaRDI QIDQ1623760

Wolfgang Pößnecker, Gerhard Tutz, Lorenz Uhlmann

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2014.09.009

zbMATH Keywords

logistic regressionvariable selectiongroup LassoLassomultinomial logit modelCATS lasso

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Generalized linear models (logistic models) (62J12)

Related Items (8)

AdaBoost Semiparametric Model Averaging Prediction for Multiple Categories ⋮ Improved nearest neighbor classifiers by weighting and selection of predictors ⋮ A modified multinomial baseline logit model with logit functions having different covariates ⋮ Simplex-based Multinomial Logistic Regression with Diverging Numbers of Categories and Covariates ⋮ Modeling Postoperative Mortality in Older Patients by Boosting Discrete-Time Competing Risks Models ⋮ Subspace quadratic regularization method for group sparse multinomial logistic regression ⋮ A classification tree approach for the modeling of competing risks in discrete time ⋮ Multiclass-penalized logistic regression

Uses Software

• R
• glmnet
• ElemStatLearn
• msgl
• catdata

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