Efficient methods for estimating constrained parameters with applications to regularized (Lasso) logistic regression

DOI10.1016/j.csda.2007.11.007zbMath1452.62550OpenAlexW2076888649WikidataQ36600435 ScholiaQ36600435MaRDI QIDQ1023690

Guo-Liang Tian, Hong-Bin Fang, Ming Tan, Man-Lai Tang

Publication date: 12 June 2009

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

Full work available at URL: http://europepmc.org/articles/pmc2352165

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Generalized linear models (logistic models) (62J12)

Related Items (5)

A novel variational Bayesian method for variable selection in logistic regression models ⋮ Double fused Lasso regularized regression with both matrix and vector valued predictors ⋮ Model selection and parameter estimation of a multinomial logistic regression model ⋮ Favorability functions based on kernel density estimation for logistic models: a case study ⋮ Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions

Uses Software

• L-BFGS-B
• L-BFGS
• LBFGS-B
• bootstrap

Cites Work

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