A unified view on Bayesian varying coefficient models
From MaRDI portal
Publication:2283580
DOI10.1214/19-EJS1653zbMATH Open1434.62092arXiv1806.02084OpenAlexW2993035141WikidataQ106514948 ScholiaQ106514948MaRDI QIDQ2283580
Author name not available (Why is that?)
Publication date: 3 January 2020
Published in: (Search for Journal in Brave)
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.
Full work available at URL: https://arxiv.org/abs/1806.02084
No records found.
No records found.
This page was built for publication: A unified view on Bayesian varying coefficient models
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q2283580)
