Discrimination and allocation using a mixture of discrete and continuous variables with some empty states.

The likelihood ratio classification rule based on the location mode is estimated given: (1) data consist of both binary and continuous variables; (2) some states have either zero frequency or too few observations, the case that usually happens the practice. An iterative proportional fitting of convenient approximation of the log-linear models as well as a linear additive model are utilized in estimating the rule's parameters. Performance of the obtained rule is then assessed by estimated error rates.