Biometrical Journal

Bootstrap and Asymptotic Prediction Criterion Estimates for Binomial Proportions in Insemination Data

Journal Article

Abstract

Model choice techniques are proposed for logistic regression, based on prediction criterion estimation similar to Akaike's information criterion. For artificial insemination data of cattle, we wish to study a factor influence on success proportion; tests standard methods don't always seem suitable for prediction objective. Two prediction criterion estimate methods are applied to these data: simulated bootstrap and asymptotic estimates. Some empirical properties of this estimate are studied.

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