  Misclassification of binary responses, if ignored, may severely bias the
maximum likelihood estimators (MLE) of regression parameters. For such data, a
binary regression model incorporating misclassification probabilities is
extensively used by researchers in different application contexts. The model,
however, suffers from a serious estimation problem because of confounding of
the unknown misclassification probabilities with the regression parameters. To
overcome this problem, in addition to the main sample, use of internal
validation data is proposed. However, the maximum likelihood estimators (MLE)
are found to be substantially biased. Investigating further, we propose a
maximum pseudo-likelihood method of estimation which leads to bias reduction.
For drawing inference on the regression parameters, we develop a rigorous
asymptotic theory for the maximum pseudo-likelihood estimators under standard
assumptions. To facilitate its easy implementation, a bootstrapped version of
the estimator is proposed, and its distributional consistency is proved.
Extensions of these results are also provided for more general
misclassification models. The results of the simulation studies are
encouraging. The methodology is illustrated with a survey data.
