Good-bootstrap: simultaneous confidence intervals for large alphabet distributions

Daniel Marton and Amichai Painsky

Journal of Nonparametric Statistics, 2024, vol. 36, issue 4, 1177-1191

Abstract: Simultaneous confidence intervals (SCI) for multinomial proportions are a corner stone in count data analysis and a key component in many applications. A variety of schemes were introduced over the years, mostly focussing on an asymptotic regime where the sample is large and the alphabet size is relatively small. In this work we introduce a new SCI framework which considers the large alphabet setup. Our proposed framework utilises bootstrap sampling with the Good-Turing probability estimator as a plug-in distribution. We demonstrate the favourable performance of our proposed method in synthetic and real-world experiments. Importantly, we provide an exact analytical expression for the bootstrapped statistic, which replaces the computationally costly sampling procedure. Our proposed framework is publicly available at the first author's Github page.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2024.2313706 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gnstxx:v:36:y:2024:i:4:p:1177-1191

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485252.2024.2313706

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().