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Cross-Modal Multivariate Pattern Analysis
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A Two-Group Classification Procedure For Multivariate Dichotomous Responses
Multivariate Behavioral Research
|January 26, 2016
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.
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