Bayesian variable selection for Poisson regression with underreported responses
AbstractVariable selection for Poisson regression when the response variable is potentially underreported is considered. A logistic regression model is used to model the latent underreporting probabilities. An efficient MCMC sampling scheme is designed, incorporating uncertainty about which explanatory variables affect the dependent variable and which affect the underreporting probabilities. Validation data is required in order to identify and estimate all parameters. A simulation study illustrates favorable results both in terms of variable selection and parameter estimation. Finally, the procedure is applied to a real data example concerning deaths from cervical cancer.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 54 (2010)
Issue (Month): 12 (December)
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Web page: http://www.elsevier.com/locate/csda
Misclassification Poisson regression MCMC Model uncertainty;
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