License: 
Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.17
URN: urn:nbn:de:0030-drops-189123
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18912/
Brunsdon, Chris ;
Harris, Paul ;
Comber, Alexis
Smarter Than Your Average Model - Bayesian Model Averaging as a Spatial Analysis Tool (Short Paper)
Abstract
Bayesian modelling averaging (BMA) allows the results of analysing competing data models to be combined, and the relative plausibility of the models to be assessed. Here, the potential to apply this approach to spatial statistical models is considered, using an example of spatially varying coefficient modelling applied to data from the 2016 UK referendum on leaving the EU.
BibTeX - Entry
@InProceedings{brunsdon_et_al:LIPIcs.GIScience.2023.17,
author = {Brunsdon, Chris and Harris, Paul and Comber, Alexis},
title = {{Smarter Than Your Average Model - Bayesian Model Averaging as a Spatial Analysis Tool}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {17:1--17:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/18912},
URN = {urn:nbn:de:0030-drops-189123},
doi = {10.4230/LIPIcs.GIScience.2023.17},
annote = {Keywords: Bayesian, Varying coefficient regression, Spatial statistics}
}
