Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics

DOI10.1080/03610918.2018.1554104zbMath1489.62211OpenAlexW2785196034WikidataQ128551754 ScholiaQ128551754MaRDI QIDQ5083965

Alvaro Veiga, Joel Corrêa da Rosa, Camila Epprecht, Dominique Guégan

Publication date: 21 June 2022

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

Full work available at URL: https://halshs.archives-ouvertes.fr/halshs-00917797v2/file/13080R.pdf

zbMATH Keywords

model selectionMonte Carlo simulationadaptive Lassosparse modelsgenetic datageneral-to-specific

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (2)

Penalised, post‐pretest, and post‐shrinkage strategies in nonlinear growth models ⋮ Variable selection in double/debiased machine learning for causal inference: an outcome-adaptive approach

Uses Software

• PcGets
• glmnet

Cites Work

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• Regularization Parameter Selections via Generalized Information Criterion
• Regularization and Variable Selection Via the Elastic Net
• Computer automation of general-to-specific model selection procedures

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