Journal article
Mean-Minimum Exact Confidence Intervals
The American statistician, Vol.71(4), pp.354-368
10/02/2017
DOI: 10.1080/00031305.2016.1256838
Abstract
This article introduces mean-minimum (MM) exact confidence intervals for a binomial probability. These intervals guarantee that both the mean and the minimum frequentist coverage never drop below specified values. For example, an MM 95[93]% interval has mean coverage at least 95% and minimum coverage at least 93%. In the conventional sense, such an interval can be viewed as an exact 93% interval that has mean coverage at least 95% or it can be viewed as an approximate 95% interval that has minimum coverage at least 93%. Graphical and numerical summaries of coverage and expected length suggest that the Blaker-based MM exact interval is an attractive alternative to, even an improvement over, commonly recommended approximate and exact intervals, including the Agresti-Coull approximate interval, the Clopper-Pearson (CP) exact interval, and the more recently recommended CP-, Blaker-, and Sterne-based mean-coverage-adjusted approximate intervals.
Details
- Title: Subtitle
- Mean-Minimum Exact Confidence Intervals
- Creators
- Joseph B. Lang - University of Iowa
- Resource Type
- Journal article
- Publication Details
- The American statistician, Vol.71(4), pp.354-368
- Publisher
- Taylor & Francis
- DOI
- 10.1080/00031305.2016.1256838
- ISSN
- 0003-1305
- eISSN
- 1537-2731
- Language
- English
- Date published
- 10/02/2017
- Academic Unit
- Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9984257736702771
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