Application of Dynamic Poisson Models to Japanese Cancer Mortality Data
Shuichi Midorikawa,Etsuo Miyaoka,Bruce R. Smith-2008-11-01-Journal of Modern Applied Statistical Methods
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TL;DRAbstract
A dynamic Poisson model is used with a Bayesian approach to modeling to predict cancer mortality. The complexity of the posterior distribution prohibits direct evaluation of the posterior, and so parameters are estimated by using a Markov Chain Monte Carlo method. The model is applied to analyze lung and stomach cancer data which have been collected in Japan.
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A dynamic Poisson model is used with a Bayesian approach to modeling to predict cancer mortality. The complexity of the posterior distribution prohibits direct evaluation of the posterior, and so parameters are estimated by using a Markov Chain Monte Carlo method. The model is applied to analyze lung and stomach cancer data which have been collected in Japan.
Keywords
Markov chain Monte CarloPoisson distributionMathematicsPoisson regressionBayesian probabilityStatisticsPosterior probabilityMonte Carlo method
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