Prior-free probabilistic interval estimation for binomial proportion

Prior-free probabilistic interval estimation for binomial proportion TEST https://doi.org/10.1007/s11749-018-0588-0 ORIGINAL PAPER Prior-free probabilistic interval estimation for binomial proportion 1 1,2 1 Hezhi Lu · Hua Jin · Zhining Wang · 1 2,3,4 Chao Chen · Ying Lu Received: 17 November 2017 / Accepted: 14 May 2018 © Sociedad de Estadística e Investigación Operativa 2018 Abstract The interval estimation of a binomial proportion has been one of the most important problems in statistical inference. The modified Wilson interval, Agresti— Coull interval, and modified Jeffreys interval have good coverage probabilities among the existing methods. However, as approximation approaches, they still behave poorly under some circumstances. In this paper, we propose an exact and efficient random- ized plausible interval based on the inference model and suggest the practical use of its non-randomized approximation. The randomized plausible interval is proven to have the exact coverage probability. Moreover, our non-randomized approximation is competitive with the existing approaches confirmed by the simulation studies. Three examples including a real data analysis are illustrated to portray the usefulness of our method. Keywords Inferential model · Binomial proportion · Interval estimation · Coverage probability · Expected length Mathematics Subject Classification 62F25 · 62P10 Hua Jin jinh1@163.com School of Mathematical Science, South China Normal University, Guangzhou http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png TEST Springer Journals

Prior-free probabilistic interval estimation for binomial proportion

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Sociedad de Estadística e Investigación Operativa
Subject
Statistics; Statistics, general; Statistical Theory and Methods; Statistics for Business/Economics/Mathematical Finance/Insurance
ISSN
1133-0686
eISSN
1863-8260
D.O.I.
10.1007/s11749-018-0588-0
Publisher site
See Article on Publisher Site

Abstract

TEST https://doi.org/10.1007/s11749-018-0588-0 ORIGINAL PAPER Prior-free probabilistic interval estimation for binomial proportion 1 1,2 1 Hezhi Lu · Hua Jin · Zhining Wang · 1 2,3,4 Chao Chen · Ying Lu Received: 17 November 2017 / Accepted: 14 May 2018 © Sociedad de Estadística e Investigación Operativa 2018 Abstract The interval estimation of a binomial proportion has been one of the most important problems in statistical inference. The modified Wilson interval, Agresti— Coull interval, and modified Jeffreys interval have good coverage probabilities among the existing methods. However, as approximation approaches, they still behave poorly under some circumstances. In this paper, we propose an exact and efficient random- ized plausible interval based on the inference model and suggest the practical use of its non-randomized approximation. The randomized plausible interval is proven to have the exact coverage probability. Moreover, our non-randomized approximation is competitive with the existing approaches confirmed by the simulation studies. Three examples including a real data analysis are illustrated to portray the usefulness of our method. Keywords Inferential model · Binomial proportion · Interval estimation · Coverage probability · Expected length Mathematics Subject Classification 62F25 · 62P10 Hua Jin jinh1@163.com School of Mathematical Science, South China Normal University, Guangzhou

Journal

TESTSpringer Journals

Published: Jun 5, 2018

References

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