Journal article
Heteroscedastic conditional auto-regression models for areally referenced temporal processes for analysing California asthma hospitalization data
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, v 64(5), pp 799-813
01 Nov 2015
PMID: 26692587
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Often in regionally aggregated spatiotemporal models, a single variance parameter is used to capture variability in the spatial structure of the model, ignoring the effect that spatially varying factors may have on the variability in the underlying process. We extend existing methodologies to allow for region-specific variance components in our analysis of monthly asthma hospitalization rates in California counties, introducing a heteroscedastic conditional auto-regression model that can greatly improve the fit of our spatiotemporal process. After demonstrating the effectiveness of our new model via simulation, we reanalyse the asthma hospitalization data and note some important findings.
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Details
- Title
- Heteroscedastic conditional auto-regression models for areally referenced temporal processes for analysing California asthma hospitalization data
- Creators
- Harrison Quick - Centers for Disease Control and PreventionBradley P. Carlin - University of MinnesotaSudipto Banerjee - Zhejiang University
- Publication Details
- JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, v 64(5), pp 799-813
- Publisher
- Wiley
- Number of pages
- 15
- Grant note
- RC1GM092400 / NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of General Medical Sciences (NIGMS) RC1 GM092400 / NIGMS NIH HHS; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of General Medical Sciences (NIGMS)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000362692500005
- Scopus ID
- 2-s2.0-84943584477
- Other Identifier
- 991020099462204721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Statistics & Probability