A statistical model investigating the prevalence of tuberculosis in New York City using counting processes with two change-points
2008 ◽
Vol 136
(12)
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pp. 1599-1605
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Keyword(s):
New York
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SUMMARYWe considered a Bayesian analysis for the prevalence of tuberculosis cases in New York City from 1970 to 2000. This counting dataset presented two change-points during this period. We modelled this counting dataset considering non-homogeneous Poisson processes in the presence of the two-change points. A Bayesian analysis for the data is considered using Markov chain Monte Carlo methods. Simulated Gibbs samples for the parameters of interest were obtained using WinBugs software.