Interval estimation for misclassification rate parameters in a complementary Poisson model

Abstract

We investigate three interval estimators for binomial misclassification rates in a complementary Poisson model where the data are possibly misclassified: a Wald-based interval, a score-based interval, and an interval based on the profile log-likelihood statistic. We investigate the coverage and average width properties of these intervals via a simulation study. For small Poisson counts and small misclassification rates, the intervals can perform poorly in terms of coverage. The profile log-likelihood confidence interval (CI) is often proved to outperform the other intervals with good coverage and width properties. Lastly, we apply the CIs to a real data set involving traffic accident data that contain misclassified counts.

Keywords:

• misclassification
• asymptotic confidence intervals
• EM algorithm
• simulation

Mathematics Subject Classification :

• 62F25
• 62N01

Acknowledgements

This work is supported in part by a research grant (114111) from the Office of Research and Sponsored Programs at Stephen F. Austin State University.