KAUST DepartmentStatistics Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/660324
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AbstractVarying 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.
CitationFranco-Villoria, M., Ventrucci, M., & Rue, H. (2019). A unified view on Bayesian varying coefficient models. Electronic Journal of Statistics, 13(2), 5334–5359. doi:10.1214/19-ejs1653
SponsorsMaria Franco-Villoria and Massimo Ventrucci are supported by the PRIN 2015 grant project n.20154X8K23 (EPHASTAT) founded by the Italian Ministry for Education, University and Research.
PublisherInstitute of Mathematical Statistics
JournalElectronic Journal of Statistics