Bayesian 2-Stage Space-Time Mixture Modeling With Spatial Misalignment of the Exposure in Small Area Health Dataopen access
- Authors
- Lawson, Andrew B.; Choi, Jungsoon; Cai, Bo; Hossain, Monir; Kirby, Russell S.; Liu, Jihong
- Issue Date
- Sep-2012
- Publisher
- American Statistical Association
- Keywords
- Air pollution; Asthma; Bayesian modeling; Covariate adjustment; Space-time mixture model
- Citation
- Journal of Agricultural, Biological, and Environmental Statistics, v.17, no.3, pp.417 - 441
- Indexed
- SCIE
SCOPUS
- Journal Title
- Journal of Agricultural, Biological, and Environmental Statistics
- Volume
- 17
- Number
- 3
- Start Page
- 417
- End Page
- 441
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/164657
- DOI
- 10.1007/s13253-012-0100-3
- ISSN
- 1085-7117
- Abstract
- We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999-2008.
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