NDLI: Objective Bayesian Model Selection in Generalized Additive Models With Penalized Splines

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Objective Bayesian Model Selection in Generalized Additive Models With Penalized Splines

Content Provider	Scilit
Author	Bové, Daniel Sabanés Held, Leonhard Kauermann, Göran
Copyright Year	2015
Description	We propose an objective Bayesian approach to the selection of covariates and their penalized splines transformations in generalized additive models. The methodology is based on a combination of continuous mixtures of g-priors for model parameters and a multiplicity-correction prior for the models themselves. We introduce our approach in the normal model and extend it to nonnormal exponential families. A simulation study and an application with binary outcome is provided. An efficient implementation is available in the R package hypergsplines. Supplementary materials for this article are available online.
Related Links	http://www.zora.uzh.ch/id/eprint/115536/1/dissPaper2.pdf
Ending Page	415
Page Count	22
Starting Page	394
ISSN	10618600
e-ISSN	15372715
DOI	10.1080/10618600.2014.912136
Journal	Journal of Computational and Graphical Statistics
Issue Number	2
Volume Number	24
Language	English
Publisher	Informa UK Limited
Publisher Date	2015-04-03
Access Restriction	Open
Subject Keyword	Journal: Journal of Computational and Graphical Statistics Statistics and Probability Function Selection G-prior Shrinkage Stochastic Search Variable Selection
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Discrete Mathematics and Combinatorics Statistics, Probability and Uncertainty

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