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arXiv · 2503.14313

Confidence Intervals Using Turing's Estimator: Simulations and Applications

Jie Chang·Michael Grabchak·Jialin Zhang
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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.

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Jie Chang, Michael Grabchak, Jialin Zhang. 2025-03-18. Confidence Intervals Using Turing's Estimator: Simulations and Applications. https://doi.org/10.1080/02664763.2026.2705587

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