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Estimation of Poisson Rates with Misclassified Counts
Author(s) -
Bratcher Thomas L.,
Stamey James D.
Publication year - 2002
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200290006
Subject(s) - poisson distribution , markov chain monte carlo , count data , statistics , estimator , bayesian probability , false positives and false negatives , mathematics , markov chain , inference , posterior probability , zero inflated model , bayesian inference , computer science , false positive paradox , econometrics , poisson regression , artificial intelligence , medicine , population , environmental health
The Poisson assumption is popular when data arises in the form of counts. In many applications such counts are fallible. Little research has been done on the Poisson distribution when both false positives and false negatives are present. We present a model in this paper that corrects for misclassification of count data. Bayesian estimators are developed. We provide the actual posterior distributions via integration. Markov Chain Monte Carlo results, which are more convenient for large sample sizes, are utilized for inference.