spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R

spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R

The R package spikeSlabGAM implements Bayesian variable selection, model choice, and regularized estimation in (geo-)additive mixed models for Gaussian, binomial, and Poisson responses. Its purpose is to (1) choose an appropriate subset of potential covariates and their interactions, (2) to determin...

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Bibliographic Details
Main Author: Fabian Scheipl
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2011-10-01
Series:Journal of Statistical Software
Subjects:
MCMC
P-splines
spike-and-slab prior
normal-inverse-gamma
Online Access:http://www.jstatsoft.org/v43/i14/paper
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spelling doaj-91c1def9475a4d32b96929f2bda20dac2020-11-24T21:01:12ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602011-10-014314spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in RFabian ScheiplThe R package spikeSlabGAM implements Bayesian variable selection, model choice, and regularized estimation in (geo-)additive mixed models for Gaussian, binomial, and Poisson responses. Its purpose is to (1) choose an appropriate subset of potential covariates and their interactions, (2) to determine whether linear or more flexible functional forms are required to model the effects of the respective covariates, and (3) to estimate their shapes. Selection and regularization of the model terms is based on a novel spike-and-slab-type prior on coefficient groups associated with parametric and semi-parametric effects.http://www.jstatsoft.org/v43/i14/paperMCMCP-splinesspike-and-slab priornormal-inverse-gamma
collection DOAJ
language English
format Article
sources DOAJ
author Fabian Scheipl
spellingShingle Fabian Scheipl
spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R
Journal of Statistical Software
MCMC
P-splines
spike-and-slab prior
normal-inverse-gamma
author_facet Fabian Scheipl
author_sort Fabian Scheipl
title spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R
title_short spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R
title_full spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R
title_fullStr spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R
title_full_unstemmed spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R
title_sort spikeslabgam: bayesian variable selection, model choice and regularization for generalized additive mixed models in r
publisher Foundation for Open Access Statistics
series Journal of Statistical Software
issn 1548-7660
publishDate 2011-10-01
description The R package spikeSlabGAM implements Bayesian variable selection, model choice, and regularized estimation in (geo-)additive mixed models for Gaussian, binomial, and Poisson responses. Its purpose is to (1) choose an appropriate subset of potential covariates and their interactions, (2) to determine whether linear or more flexible functional forms are required to model the effects of the respective covariates, and (3) to estimate their shapes. Selection and regularization of the model terms is based on a novel spike-and-slab-type prior on coefficient groups associated with parametric and semi-parametric effects.
topic MCMC
P-splines
spike-and-slab prior
normal-inverse-gamma
url http://www.jstatsoft.org/v43/i14/paper
work_keys_str_mv AT fabianscheipl spikeslabgambayesianvariableselectionmodelchoiceandregularizationforgeneralizedadditivemixedmodelsinr
_version_ 1716778498564030464

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