Penalized likelihood and Bayesian function selection in regression models

DOI10.1007/s10182-013-0211-3zbMath1443.62106arXiv1303.0670OpenAlexW1976370576MaRDI QIDQ1621251

Ludwig Fahrmeir, Thomas Kneib, Fabian Scheipl

Publication date: 8 November 2018

Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1303.0670

zbMATH Keywords

smoothingregularizationgeneralized additive modelspike and slab priors

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

Mean and quantile boosting for partially linear additive models, Bayesian ridge regression for survival data based on a vine copula-based prior, Bayesian Semiparametric Functional Mixed Models for Serially Correlated Functional Data, With Application to Glaucoma Data, Bayesian ridge estimators based on copula-based joint prior distributions for regression coefficients

Uses Software

• Matlab
• Octave
• R
• UCI-ml
• gss
• BayesX
• mboost
• spikeSlabGAM
• mgcv
• grplasso
• hgam
• R2BayesX
• hypergsplines

Cites Work

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