文章基本信息

标题：A unified view on Bayesian varying coefficient models
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作者：Maria Franco-Villoria ; Massimo Ventrucci ; Håvard Rue 等
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2019
卷号：13
期号：2
页码：5334-5359
DOI：10.1214/19-EJS1653
语种：English
出版社：Institute of Mathematical Statistics
摘要：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.
关键词：INLA; overfitting; penalized complexity prior; varying coefficient models