  Naive Bayes classifiers have proven to be useful in many prediction problems
with complete training data. Here we consider the situation where a naive Bayes
classifier is trained with data where the response is right censored. Such
prediction problems are for instance encountered in profiling systems used at
National Employment Agencies. In this paper we propose the maximum collective
conditional likelihood estimator for the prediction and show that it is
strongly consistent under the usual identifiability condition.
