A unified view on Bayesian varying coefficient models

Maria Franco-Villoria, Massimo Ventrucci, Haavard Rue

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)5334-5359
Number of pages26
JournalElectronic Journal of Statistics
Issue number2
StatePublished - Dec 28 2019


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