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Hyper-g priors for generalised additive model selection


Sabanés Bové, D; Held, L; Kauermann, G (2011). Hyper-g priors for generalised additive model selection. In: 26th International Workshop on Statistical Modelling, Valencia, Spain, 10 July 2011 - 15 July 2011, 538-543.

Abstract

We propose an automatic Bayesian approach to the selection of covariates and penalised splines transformations thereof in generalised additive models. Specification of a 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 illustrate it with an application to diabetes data. Extension to non-normal exponential families is finally discussed.

Abstract

We propose an automatic Bayesian approach to the selection of covariates and penalised splines transformations thereof in generalised additive models. Specification of a 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 illustrate it with an application to diabetes data. Extension to non-normal exponential families is finally discussed.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), not refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Event End Date:15 July 2011
Deposited On:29 Dec 2011 13:28
Last Modified:15 Dec 2017 06:28
Related URLs:http://www.geeitema.org/iwsm2011/

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