Maximizing the Accuracy of Probit Models via UniODA

Maximizing the Accuracy of Probit Models via UniODA 

Barbara M. Yarnold, J.D., Ph.D. and Paul R. Yarnold, Ph.D.

Optimal Data Analysis, LLC

Paralleling the procedure used to maximize ESS of linear models derived using logistic regression analysis or Fisher’s discriminant analysis, univariate optimal discriminant analysis (UniODA) is applied to the predicted response function values provided by a
model derived by probit analysis (PA), and returns an adjusted decision criterion for making classification decisions. ESS obtains its theoretical maximum value with this adjusted decision criterion, and the ability of the PA model to return accurate classifications is optimized. UniODA-refinement of a PA model is illustrated using an example involving political science analysis of federal courts.

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