Unsupervised classification models with non-random missing data.

Summary The difficulty of accounting for missing data is often con-tained by assuming that their occurrence is due to chance. In this paper, we consider that the absence of some data is not due to chance in the context of unsupervised classification and we propose logistic models to reflect the fact that this occurrence can be associated with the sought classification. We focus on different models that we estimate by maximum likelihood and we analyze their characteristics through their application on hospital data.