  The Akaike information criterion (AIC) is commonly used to select a logistic
regression model for optimal prediction of a binary response by a specified
family of models. It however lacks a convincing method of prescribing a proper
family of models using the desired predictors and their interaction effects.
For an alternative approach to model selection, we propose a direct selection
scheme which first identifies the indispensable regressors as main-effect
predictors, then examines significant interaction effects between the selected
predictors such that a logistic model is constructed. The two-step selection
scheme is formulated by testing for valid information identity between the
response and the predictors, from which the most parsimonious logistic model is
derived from the least set of indispensable predictors and interaction effects.
As a byproduct, the minimum AIC model is easily found in a neighborhood of the
selected model. The scheme is employed to yield the logistic model for
predicting the acquisition of professional licenses in a survey of employed
youth workers.
