Abstract
The plug-in rule is used for the classification of random observations into one of two regular one-parametric distributions. The maximum likelihood estimates of unknown parameters obtained from the stratified training sample are used. The second-order asymptotic expansion in terms of the inverses of the training sample sizes is derived for the expected regret risk. The closed-form expressions of the expansion coefficients are applicable for the performance evaluation of the proposed classification rule.
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