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dc.contributor.authorCongdon, Pen_US
dc.date.accessioned2020-06-22T13:49:57Z
dc.date.available2020-03-02en_US
dc.date.issued2020-04-01en_US
dc.identifier.issn1574-1699en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/65111
dc.description.abstract© 2020 - IOS Press and the authors. All rights reserved. Sparsity inducing priors are widely used in Bayesian regression analysis, and seek dimensionality reduction to avoid unnecessarily complex models. An alternative to sparsity induction are discrete mixtures, such as spike and slab priors. These ideas extend to selection of random effects, either i?i?d or structured (e.g. spatially structured). In contrast to sparsity induction in mixed models with i?i?d random effects, in this paper we apply sparsity priors to spatial regression for area units (lattice data), and to spatial random effects in conditional autoregressive priors. In particular, we consider the use of global-local shrinkage to distinguish areas with average predictor effects from areas where the predictor effect is amplified or diminished because the response-predictor pattern is distinct from that of most areas. The operation and utility of this approach is demonstrated using simulated data, and in a real application to diabetes related deaths in New York counties.en_US
dc.format.extent99 - 109en_US
dc.relation.ispartofModel Assisted Statistics and Applicationsen_US
dc.titleModelling spatially varying coefficients via sparsity priorsen_US
dc.typeArticle
dc.identifier.doi10.3233/MAS-200481en_US
pubs.issue2en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume15en_US
dcterms.dateAccepted2020-03-02en_US


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