Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23816

Title: Spatio-temporal Bayesian model selection for disease mapping
Authors: Carroll, Rachel
Lawson, Andrew B.
Faes, Christel
Kirby, Russell S.
Aregay, Mehreteab
Watjou, Kevin
Issue Date: 2016
Citation: ENVIRONMETRICS, 27(8), p. 466-478
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.
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.
URI: http://hdl.handle.net/1942/23816
DOI: 10.1002/env.2410
ISI #: 000392948100002
ISSN: 1180-4009
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Published version728.04 kBAdobe PDF
Peer-reviewed author version698.66 kBAdobe PDF
Peer-reviewed author version682.33 kBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.