Variable selection for generalized linear mixed models by L 1-penalized estimation

Andreas Groll; Gerhard Tutz

doi:10.1007/s11222-012-9359-z

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Variable selection for generalized linear mixed models by L 1-penalized estimation

Andreas Groll Gerhard Tutz

Year: 2012 Journal: Statistics and Computing Vol: 24 (2)Pages: 137-154 Publisher: Springer Science+Business Media

DOI: 10.1007/s11222-012-9359-z

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Keywords:

Generalized linear model Mathematics Covariate Generalized linear mixed model Feature selection Contrast (vision) Model selection Linear model Mixed model Lasso (programming language) Term (time) Variable (mathematics) Applied mathematics Algorithm Mathematical optimization Computer science Statistics Artificial intelligence

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Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Statistical Methods and Bayesian Inference

Physical Sciences → Mathematics → Statistics and Probability

Bayesian Methods and Mixture Models

Physical Sciences → Computer Science → Artificial Intelligence

Variable selection for generalized linear mixed models by L 1-penalized estimation

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