BaySTDetect: detecting unusual temporal patterns in small area data via Bayesian model choice.

@article{Li2012BaySTDetectDU,
  title={BaySTDetect: detecting unusual temporal patterns in small area data via Bayesian model choice.},
  author={Guangquan Li and Nicky Best and Anna L Hansell and Isma{\"i}l Ahmed and Sylvia Richardson},
  journal={Biostatistics},
  year={2012},
  volume={13 4},
  pages={695-710}
}
Space-time modeling of small area data is often used in epidemiology for mapping chronic disease rates and by government statistical agencies for producing local estimates of, for example, unemployment or crime rates. Although there is typically a general temporal trend, which affects all areas similarly, abrupt changes may occur in a particular area, e.g. due to emergence of localized predictors/risk factor(s) or impact of a new policy. Detection of areas with "unusual" temporal patterns is… CONTINUE READING
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