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2019 A unified view on Bayesian varying coefficient models
Maria Franco-Villoria, Massimo Ventrucci, Håvard Rue
Electron. J. Statist. 13(2): 5334-5359 (2019). DOI: 10.1214/19-EJS1653


Varying coefficient models are useful in applications where the effect of the covariate might depend on some other covariate such as time or location. Various applications of these models often give rise to case-specific prior distributions for the parameter(s) describing how much the coefficients vary. In this work, we introduce a unified view of varying coefficients models, arguing for a way of specifying these prior distributions that are coherent across various applications, avoid overfitting and have a coherent interpretation. We do this by considering varying coefficients models as a flexible extension of the natural simpler model and capitalising on the recently proposed framework of penalized complexity (PC) priors. We illustrate our approach in two spatial examples where varying coefficient models are relevant.


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Maria Franco-Villoria. Massimo Ventrucci. Håvard Rue. "A unified view on Bayesian varying coefficient models." Electron. J. Statist. 13 (2) 5334 - 5359, 2019.


Received: 1 December 2018; Published: 2019
First available in Project Euclid: 28 December 2019

zbMATH: 07147378
MathSciNet: MR4047589
Digital Object Identifier: 10.1214/19-EJS1653

Keywords: INLA , overfitting , penalized complexity prior , varying coefficient models


Vol.13 • No. 2 • 2019
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