Heteroscedastic Conditional Auto-Regression Models for Areally Referenced Temporal Processes for Analysing California Asthma Hospitalization Data

Details

• Personal Author:
  Banerjee S ; Carlin BP ; Quick H
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Asthma Asthma, Occupational Occupational Health Safety
• Keywords:
  Author Keywords: Bayesian Methods Exposure Assessment Gaussian Process Gradients Markov Chain Monte Carlo Methods Models Spatial Process Models Statistical Analysis
• ISSN:
  0035-9254
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; Georgia ; Minnesota ; OSHA Region 4 ; OSHA Region 5 ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  64
• Issue:
  5
• NIOSHTIC Number:
  nn:20055681
• Citation:
  J R Stat Soc Ser C Appl Stat 2015 Nov; 64(5):799-813
• Contact Point Address:
  Harrison Quick, Division of Heart Disease and Stroke Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA
• Email:
  HQuick@cdc.gov
• Federal Fiscal Year:
  2016
• Performing Organization:
  University of California, Los Angeles
• Peer Reviewed:
  False
• Start Date:
  20130901
• Source Full Name:
  Journal of the Royal Statistical Society. Series C, Applied Statistics
• End Date:
  20170831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:77f1a4f6a30b8804cfe4a042f4b05e3fa3eea79e4f33041ed1fcb74cc5c5ae19b4e0854a66d834f0301910dbf82d88366f28452ea93e111d99ffc544a523e654
• Download URL:
  https://stacks.cdc.gov/view/cdc/213471/cdc_213471_DS1.pdf
• File Type:
  [PDF - 1.08 MB ]