Confidence Intervals Using Turing's Estimator: Simulations and Applications

Fuente: arXiv
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Main Authors: Chang, Jie, Grabchak, Michael, Zhang, Jialin
Format: Preprint
Published: 2025
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author Chang, Jie
Grabchak, Michael
Zhang, Jialin
author_facet Chang, Jie
Grabchak, Michael
Zhang, Jialin
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2503_14313
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Confidence Intervals Using Turing's Estimator: Simulations and Applications
Chang, Jie
Grabchak, Michael
Zhang, Jialin
Statistics Theory
Applications
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.
title Confidence Intervals Using Turing's Estimator: Simulations and Applications
topic Statistics Theory
Applications
url https://arxiv.org/abs/2503.14313