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Improving interval estimation of binomial proportions
In this paper, we propose one new confidence interval for the binomial proportion; our interval is based on the Edgeworth expansion of a logit transformation of the sample proportion. We provide theoretical justification for the proposed interval and also compare the finite-sample performance of the...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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The Royal Society
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2706447/ https://www.ncbi.nlm.nih.gov/pubmed/18407898 http://dx.doi.org/10.1098/rsta.2008.0037 |
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author | Zhou, X.H. Li, C.M. Yang, Z. |
author_facet | Zhou, X.H. Li, C.M. Yang, Z. |
author_sort | Zhou, X.H. |
collection | PubMed |
description | In this paper, we propose one new confidence interval for the binomial proportion; our interval is based on the Edgeworth expansion of a logit transformation of the sample proportion. We provide theoretical justification for the proposed interval and also compare the finite-sample performance of the proposed interval with the three best existing intervals—the Wilson interval, the Agresti–Coull interval and the Jeffreys interval—in terms of their coverage probabilities and expected lengths. We illustrate the proposed method in two real clinical studies. |
format | Text |
id | pubmed-2706447 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-27064472009-08-03 Improving interval estimation of binomial proportions Zhou, X.H. Li, C.M. Yang, Z. Philos Trans A Math Phys Eng Sci Research Article In this paper, we propose one new confidence interval for the binomial proportion; our interval is based on the Edgeworth expansion of a logit transformation of the sample proportion. We provide theoretical justification for the proposed interval and also compare the finite-sample performance of the proposed interval with the three best existing intervals—the Wilson interval, the Agresti–Coull interval and the Jeffreys interval—in terms of their coverage probabilities and expected lengths. We illustrate the proposed method in two real clinical studies. The Royal Society 2008-04-11 2008-07-13 /pmc/articles/PMC2706447/ /pubmed/18407898 http://dx.doi.org/10.1098/rsta.2008.0037 Text en Copyright © 2008 The Royal Society http://creativecommons.org/licenses/by/2.5/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhou, X.H. Li, C.M. Yang, Z. Improving interval estimation of binomial proportions |
title | Improving interval estimation of binomial proportions |
title_full | Improving interval estimation of binomial proportions |
title_fullStr | Improving interval estimation of binomial proportions |
title_full_unstemmed | Improving interval estimation of binomial proportions |
title_short | Improving interval estimation of binomial proportions |
title_sort | improving interval estimation of binomial proportions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2706447/ https://www.ncbi.nlm.nih.gov/pubmed/18407898 http://dx.doi.org/10.1098/rsta.2008.0037 |
work_keys_str_mv | AT zhouxh improvingintervalestimationofbinomialproportions AT licm improvingintervalestimationofbinomialproportions AT yangz improvingintervalestimationofbinomialproportions |