A note on intrinsic conditional autoregressive models for disconnected graphs
Type
Article
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2018-05-23Preprint Posting Date
2017-05-13Online Publication Date
2018-05-23Print Publication Date
2018-08Permanent link to this record
http://hdl.handle.net/10754/626456Metadata
Show full item recordAbstract
In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping.Citation
Freni-Sterrantino A, Ventrucci M, Rue H (2018) A note on intrinsic conditional autoregressive models for disconnected graphs. Spatial and Spatio-temporal Epidemiology 26: 25–34. Available: http://dx.doi.org/10.1016/j.sste.2018.04.002.Sponsors
We thank Dr M. A. Vigotti (University of Pisa) for having made available the dataset from the Tuscany Atlas of Mortality 1971–1994. Massimo Ventrucci is supported by the PRIN 2015 grant project n.20154X8K23 (EPHASTAT) founded by the Italian Ministry for Education, University and Research. We can provide the code for the examples.Publisher
Elsevier BVarXiv
1705.04854Additional Links
http://www.sciencedirect.com/science/article/pii/S1877584517301600Scopus Count
![NOTICE: this is the author’s version of a work that was accepted for publication in Spatial and Spatio-temporal Epidemiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial and Spatio-temporal Epidemiology, [26, (2018)] DOI: 10.1016/j.sste.2018.04.002. © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/](/themes/OR//images/creativecommons/cc-by-nc-nd.png)
Except where otherwise noted, this item's license is described as NOTICE: this is the author’s version of a work that was accepted for publication in Spatial and Spatio-temporal Epidemiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial and Spatio-temporal Epidemiology, [26, (2018)] DOI: 10.1016/j.sste.2018.04.002. © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/