Type
ArticleKAUST Department
Statistics ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2019-12-28Submitted Date
2018-12-01Permanent link to this record
http://hdl.handle.net/10754/660324
Metadata
Show full item recordAbstract
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.Citation
Franco-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-ejs1653Sponsors
Maria 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.Publisher
Institute of Mathematical StatisticsJournal
Electronic Journal of StatisticsarXiv
1806.02084Additional Links
https://projecteuclid.org/euclid.ejs/1577502094ae974a485f413a2113503eed53cd6c53
10.1214/19-EJS1653