title: A Two-Group Classification Procedure For Multivariate Dichotomous Responses.
abstract: A two-group classification procedure for multivariate binary data is presented and discussed for the case of independent samples. The rule utilizes an orthogonal series expansion for state probabilities and introduces a method to achieve parsimony in modeling in accordance with a condition of optimality. In addition, the classification procedure gives a method for dealing with the difficult problem of sparseness, a condition which frequently causes problems in applying discrete methods to frequency data.
authors.forename: M
authors.initials: M
authors.lastname: Goldstein