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http://hdl.handle.net/1942/23816
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DC Field | Value | Language |
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dc.contributor.author | Carroll, Rachel | - |
dc.contributor.author | LAWSON, Andrew | - |
dc.contributor.author | FAES, Christel | - |
dc.contributor.author | Kirby, Russell S. | - |
dc.contributor.author | AREGAY, Mehreteab | - |
dc.contributor.author | WATJOU, Kevin | - |
dc.date.accessioned | 2017-05-23T14:44:21Z | - |
dc.date.available | 2017-05-23T14:44:21Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | ENVIRONMETRICS, 27(8), p. 466-478 | - |
dc.identifier.issn | 1180-4009 | - |
dc.identifier.uri | http://hdl.handle.net/1942/23816 | - |
dc.description.abstract | Spatio-temporal analysis of small area health data often involves choosing a fixed set of predictors prior to the final model fit. In this paper, we propose a spatio-temporal approach of Bayesian model selection to implement model selection for certain areas of the study region as well as certain years in the study time line. Here, we examine the usefulness of this approach by way of a large-scale simulation study accompanied by a case study. Our results suggest that a special case of the model selection methods, a mixture model allowing a weight parameter to indicate if the appropriate linear predictor is spatial, spatio-temporal, or a mixture of the two, offers the best option to fitting these spatio-temporal models. In addition, the case study illustrates the effectiveness of this mixture model within the model selection setting by easily accommodating lifestyle, socio-economic, and physical environmental variables to select a predominantly spatio-temporal linear predictor. | - |
dc.description.sponsorship | This research was supported in part by funding under grant NIH R01CA172805. | - |
dc.language.iso | en | - |
dc.publisher | WILEY-BLACKWELL | - |
dc.rights | Copyright © 2016 John Wiley & Sons, Ltd. | - |
dc.subject.other | BRugs; MCMC; melanoma; model selection; Poisson | - |
dc.subject.other | BRugs; MCMC; melanoma; model selection; Poisson | - |
dc.title | Spatio-temporal Bayesian model selection for disease mapping | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 478 | - |
dc.identifier.issue | 8 | - |
dc.identifier.spage | 466 | - |
dc.identifier.volume | 27 | - |
local.format.pages | 13 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | [Carroll, Rachel; Lawson, Andrew B.; Aregay, Mehreteab] Med Univ South Carolina, Dept Publ Hlth Sci, Charleston, SC USA. [Faes, Christel; Watjou, Kevin] Hasselt Univ, Interuniv Inst Stat & Stat Bioinformat, Hasselt, Belgium. [Kirby, Russell S.] Univ S Florida, Dept Community & Family Hlth, Tampa, FL USA. | - |
local.publisher.place | HOBOKEN | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1002/env.2410 | - |
dc.identifier.isi | 000392948100002 | - |
item.validation | ecoom 2018 | - |
item.fulltext | With Fulltext | - |
item.accessRights | Open Access | - |
item.fullcitation | Carroll, Rachel; LAWSON, Andrew; FAES, Christel; Kirby, Russell S.; AREGAY, Mehreteab & WATJOU, Kevin (2016) Spatio-temporal Bayesian model selection for disease mapping. In: ENVIRONMETRICS, 27(8), p. 466-478. | - |
item.contributor | Carroll, Rachel | - |
item.contributor | LAWSON, Andrew | - |
item.contributor | FAES, Christel | - |
item.contributor | Kirby, Russell S. | - |
item.contributor | AREGAY, Mehreteab | - |
item.contributor | WATJOU, Kevin | - |
crisitem.journal.issn | 1180-4009 | - |
crisitem.journal.eissn | 1099-095X | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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carolle 1.pdf Restricted Access | Published version | 728.04 kB | Adobe PDF | View/Open Request a copy |
ST_MS_aim3_revised_altlinpredsectMOVED_062716.pdf | Peer-reviewed author version | 674.86 kB | Adobe PDF | View/Open |
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