Abstract:Turing's estimator allows one to estimate the probabilities of outcomes that either do not appear or only rarely appear in a given random sample. We perform a simulation study to understand the finite sample performance of several related confidence intervals (CIs) and introduce an approach for selecting the appropriate CI for a given sample. We give an application to the problem of authorship attribution and apply it to a dataset comprised of tweets from users on X (Twitter). Further, we derive several theoretical results about asymptotic normality and asymptotic Poissonity of Turing's estimator for two important discrete distributions.
From: Jialin Zhang [view email]
[v1]
Tue, 18 Mar 2025 14:48:57 UTC (1,228 KB)
[v2]
Fri, 10 Jul 2026 13:28:28 UTC (1,331 KB)
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