Varying coefficient models arise naturally as a flexible extension of a simpler model where the effect of the covariate is constant. In this work, we present varying coefficient models in a unified way using the recently proposed framework of penalized complexity (PC) priors to build priors that allow proper shrinkage to the simpler model, avoiding overfitting. We illustrate their application in two spatial examples where varying coefficient models are relevant.

Bayesian varying coefficient models using PC priors

Maria Franco-Villoria;
2018-01-01

Abstract

Varying coefficient models arise naturally as a flexible extension of a simpler model where the effect of the covariate is constant. In this work, we present varying coefficient models in a unified way using the recently proposed framework of penalized complexity (PC) priors to build priors that allow proper shrinkage to the simpler model, avoiding overfitting. We illustrate their application in two spatial examples where varying coefficient models are relevant.
2018
http://arxiv.org/abs/1806.02084v1
Statistics - Methodology; Statistics - Methodology
Maria Franco-Villoria; Massimo Ventrucci; Håvard Rue
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1689638
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