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arXiv · 1108.3520

Mixtures of g-Priors for Generalised Additive Model Selection with Penalised Splines

Daniel Sabanés Bové·Leonhard Held·Göran Kauermann
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Abstract

We propose an objective Bayesian approach to the selection of covariates and their penalised splines transformations in generalised additive models. Specification of a reasonable default prior for the model parameters and combination with a multiplicity-correction prior for the models themselves is crucial for this task. Here we use well-studied and well-behaved continuous mixtures of g-priors as default priors. We introduce the methodology in the normal model and extend it to non-normal exponential families. A simulation study and an application from the literature illustrate the proposed approach. An efficient implementation is available in the R-package "hypergsplines".

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Daniel Sabanés Bové, Leonhard Held, Göran Kauermann. 2011-08-17. Mixtures of g-Priors for Generalised Additive Model Selection with Penalised Splines. https://arxiv.org/abs/1108.3520

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