On inference from general categorical data with misclassification errors based on double sampling schemes.

In order to resolve the difficulties involved in inference from a sample of categorical data obtained by using a fallible classifying mechanism (usually inexpensive), we consider the utilization of a sub-sample subjected to a simultaneous cross classification of its elements by the fallible mechanism and by some true (usually expensive) classifying mechanism. Two methodologies are presented: (1) maximum likelihood approach; and (2) least squares approach. Both methodologies are illustrated.