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MAXIMUM LIKELIHOOD ESTIMATION FOR A POISSON RATE PARAMETER WITH MISCLASSIFIED COUNTS

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Summary

This paper proposes a Poisson-based model that uses both error-free data and error-prone data subject to misclassification in the form of false-negative and false-positive counts. It derives maximum likelihood estimators (MLEs) for the Poisson rate parameter and the two misclassification parameters — the false-negative parameter and the false-positive parameter. It also derives expressions for the information matrix and the asymptotic variances of the MLE for the rate parameter, the MLE for the false-positive parameter, and the MLE for the false-negative parameter. Using these expressions the paper analyses the value of the fallible data. It studies characteristics of the new double-sampling rate estimator via a simulation experiment and applies the new MLE estimators and confidence intervals to a real dataset.
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Keywords: Fisher's information matrix; asymptotic variance; double sample

Document Type: Research Article

Affiliations: Stephen F. Austin University and Baylor University

Publication date: June 1, 2005

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