Bayesian Predictive Probability Functions for Count Data that are Subject to Misclassification |
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Authors: | James D Stamey Dean M Young Tom L Bratcher |
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Abstract: | We develop three Bayesian predictive probability functions based on data in the form of a double sample. One Bayesian predictive probability function is for predicting the true unobservable count of interest in a future sample for a Poisson model with data subject to misclassification and two Bayesian predictive probability functions for predicting the number of misclassified counts in a current observable fallible count for an event of interest. We formulate a Gibbs sampler to calculate prediction intervals for these three unobservable random variables and apply our new predictive models to calculate prediction intervals for a real‐data example. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim) |
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Keywords: | Poisson distribution Binomial distribution False‐positive observations False‐negative observations |
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