id: 06095872
dt: j
an: 06095872
au: Zhuang, Lili; Cressie, Noel
ti: Spatio-temporal modeling of sudden infant death syndrome data.
so: Stat. Methodol. 9, No. 1-2, 117-143 (2012).
py: 2012
pu: Elsevier, Amsterdam
la: EN
cc: 
ut: dynamical Markov random field; SIDS
ci: 
li: doi:10.1016/j.stamet.2011.01.006
ab: Summary: 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. We analyze a spatio-temporal data
    set that contains yearly SIDS information from 1979 to 1984 for the
    counties of North Carolina. {\it N. Cressie} and {\it N.H. Chan} [J.
    Am. Stat. Assoc. 84, No. 406, 393‒401 (1989; Zbl 06095574)] used a
    purely spatial model to analyze the aggregated version of this data
    set. 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.
rv: