Extension of model-based classification for binary data when training and test populations differ

Standard discriminant analysis supposes that both the training sample and the test sample are derived from the same population. When these samples arise from populations differing in their descriptive parameters, a generalization of discriminant analysis consists of adapting the classification rule related to the training population to another rule related to the test population, by estimating a link map between both populations. This paper extends an existing work in the multinormal context to the case of binary data. In order to solve the problem of defining a link map between the two binary populations, it is assumed that the binary data result from the discretization of latent Gaussian data. An estimation method and a robustness study are presented, and two applications in a biological context illustrate this work.