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    A unified view on Bayesian varying coefficient models

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    Type
    Article
    Authors
    Franco-Villoria, Maria
    Ventrucci, Massimo
    Rue, Haavard cc
    KAUST Department
    Statistics Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-12-28
    Submitted Date
    2018-12-01
    Permanent link to this record
    http://hdl.handle.net/10754/660324
    
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    Abstract
    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-ejs1653
    Sponsors
    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 Statistics
    Journal
    Electronic Journal of Statistics
    DOI
    10.1214/19-EJS1653
    arXiv
    1806.02084
    Additional Links
    https://projecteuclid.org/euclid.ejs/1577502094
    ae974a485f413a2113503eed53cd6c53
    10.1214/19-EJS1653
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