Selection of tuning parameters in bridge regression models via Bayesian information criterion

DOI10.1007/s00362-013-0561-7zbMath1297.62152arXiv1203.4326OpenAlexW3105452317MaRDI QIDQ465645

Shuichi Kawano

Publication date: 24 October 2014

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1203.4326

zbMATH Keywords

model selectionsparse regressionbridge penaltypenalized maximum likelihood method

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Nonparametric estimation (62G05) Bayesian inference (62F15)

Related Items (4)

The minimum \(S\)-divergence estimator under continuous models: the Basu-Lindsay approach ⋮ Deterministic bridge regression for compressive classification ⋮ Penalized MM regression estimation withL_γpenalty: a robust version of bridge regression ⋮ Degrees of freedom for regularized regression with Huber loss and linear constraints

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