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Spatio-temporal modeling of sudden infant death syndrome data

Journal Article


Abstract


  • Sudden infant death syndrome (SIDS) is a classification of death for apparently healthy infants under one year old. However, its etiology is still largely a mystery. In this research, we analyze a spatio-temporal data set that contains yearly SIDS information from 1979 to 1984 for the counties of North Carolina. Cressie and Chan (1989) [10] used a purely spatial model to analyze the aggregated version of this data set. In this article, we present a spatio-temporal model from which optimal smoothing of SIDS rates can be derived. We use a Bayesian hierarchical statistical model (BHM) with a hidden dynamical Markov random field and extra-Poisson variability. Potential confounding of sources of variability is avoided by calibrating the extra-Poisson variability with the microscale variation in an approximate Gaussian model. © 2011 Elsevier B.V..

Publication Date


  • 2012

Citation


  • Zhuang, L. & Cressie, N. A. (2012). Spatio-temporal modeling of sudden infant death syndrome data. Statistical Methodology, 9 (1-2), 117-143.

Scopus Eid


  • 2-s2.0-81255149551

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2266

Number Of Pages


  • 26

Start Page


  • 117

End Page


  • 143

Volume


  • 9

Issue


  • 1-2

Abstract


  • Sudden infant death syndrome (SIDS) is a classification of death for apparently healthy infants under one year old. However, its etiology is still largely a mystery. In this research, we analyze a spatio-temporal data set that contains yearly SIDS information from 1979 to 1984 for the counties of North Carolina. Cressie and Chan (1989) [10] used a purely spatial model to analyze the aggregated version of this data set. In this article, we present a spatio-temporal model from which optimal smoothing of SIDS rates can be derived. We use a Bayesian hierarchical statistical model (BHM) with a hidden dynamical Markov random field and extra-Poisson variability. Potential confounding of sources of variability is avoided by calibrating the extra-Poisson variability with the microscale variation in an approximate Gaussian model. © 2011 Elsevier B.V..

Publication Date


  • 2012

Citation


  • Zhuang, L. & Cressie, N. A. (2012). Spatio-temporal modeling of sudden infant death syndrome data. Statistical Methodology, 9 (1-2), 117-143.

Scopus Eid


  • 2-s2.0-81255149551

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2266

Number Of Pages


  • 26

Start Page


  • 117

End Page


  • 143

Volume


  • 9

Issue


  • 1-2