Maximum likelihood estimation of odds ratios in misclassified binary data with a validation substudy
Abstract
We consider misclassified binary data with a validation substudy. For such data various methods have been developed for estimating the odds ratio. It is well-known that the maximum likelihood estimator (MLE) of the odds ratio is efficient but requires iterative algorithms to compute. In this article, we derive a closed-form formula for the MLE and its asymptotic standard error. We compute the closed-form MLE on a data set that has been analyzed by other methods, and the results are compared. © 2011 - IOS Press and the authors. All rights reserved.
Publication Title
Model Assisted Statistics and Applications
Recommended Citation
Rahardja, D., Zhao, Y., & Zhang, H. (2011). Maximum likelihood estimation of odds ratios in misclassified binary data with a validation substudy. Model Assisted Statistics and Applications, 6 (2), 121-125. https://doi.org/10.3233/MAS-2011-0184
