Abstract
This paper derives identification errors in a moderate deviations framework. It provides a new perspective to understand the fundamental relationship between probabilistic errors and resources that represent data sizes in computational algorithms, sample sizes in statistical analysis, channel bandwidths in communications, etc. This relationship is derived by establishing the moderate deviations principle (MDP) for regular and binary identification. Under some mild conditions, we obtain moderate deviations upper and lower bounds for regular and binary observations respectively.
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