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In this paper we consider use of some special log-linear models and minimum delta estimation in the multivariate classification problem posed by Martin and Bradley (1972). We first define these models, called log-difference models, and show that the minimum risk classification rule depends only on a certain subset of the new parameters. We then review minimum delta estimation, in particular the minimum delta estimator, the approximate minimum delta estimator, and their existence properties. Two examples are worked. The first involves detergent preference and illustrates how extensions to the case in which not all variables are dichotomous may be obtained through the use of orthogonal polynomials. The second example involves infant hypoxic trauma, and many cells are empty. The existence conditions are used to find a model for which estimates a cell frequencies exist and are in good agreement with the observed data. (Author)

Two new methods for estimation of parameters in log-linear models are proposed and their properties considered in this article. Conditions for the existence of the new estimators are derived, and the new estimators are shown to possess appropriate asymptotic properties.

The Phase II study used storms as the basic experimental unit whereas the Phase I study used the convective band. Difficulties arise in analyses because of this change. The covariates were again measured in the area of expected response from seeding and hence are suspect.