Classification of dichotomous and continuous variables with incomplete samples

This Article Considers the problem of classifiying an observation consisting of both binary and continuous variables based on two general incomplete training samples one from each of the two given populations. The location linear model adopted by krzanowski 1975 forms the basis of our inverstigation. For a given location, When the common dispersion matrix as Well as the corresponding cell probabilities for the underlying populations are known, exact distribution of the conditional maximum likelihood classification rule is derived. The overall error rate can be obtained and is based on linear cominations of independent non– Chi– Distributions. large sample result for the case where the cell probabilities are unknown is also available.