Estimating the Expected and the Optimal Probabilities of Misclassification

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Abstract
The problem is to estimate the expected and the optimal probabilities of misclassification in the context of the two group p-dimensional normal classification problem with means and common covariance matrix unknown and a rule based on the linear discriminant function. Performance of several estimators is compared by means of a computer sampling study. For larger p (p = 20) certain estimators are definitely superior for each of the probabilities, while for small p there is less differentiation in performance.

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