Hyper-g Priors for Generalised Additive Model Selection with Penalised Splines

Details Hyper-g Priors for Generalised Additive Model Selection with Penalised Splines Hyper-g Priors for Generalised Additive Model Selection with Penalised Splines

2011-08-17

arxiv.org/abs/1108.3520v1

Statistics

Methodology

33 pages, 3 figures, 4 tables

Scientific paper

We propose an automatic Bayesian approach to the selection of covariates and their penalised splines transformations in generalised additive models. Specification of a default, hyper-g prior for the model parameters and a multiplicity-correction prior for the models themselves is crucial for this task. We introduce the methodology in the normal model and extend it to non-normal exponential families. Two applications from the literature illustrate the proposed approach. An efficient implementation is available in an R-package.

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