SIMULTANEOUS CONFIDENCE INTERVALS FOR MULTINOMIAL PROPORTIONS, THEIR DIFFERENCES AND RATIOS
2010
- 46Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage46
- Downloads34
- Abstract Views12
Thesis / Dissertation Description
Estimation of simultaneous confidence intervals for multinomial proportions, their differences and ratios is a long-standing problem in the literature. Existing methods suffer either from enforced symmetry and/or conservative coverage. We propose calculating confidence limits using the Wilson method or Jeffrey’s procedure for the multinomial proportions and for ratios of multinomial proportions with critical values obtained from multivariate normal distributions that accounts for correlations among proportions and contrasts. Confidence intervals for the contrasts are then obtained by recovering variance estimates from limits for single proportions. Simulation study shows that proposed methods perform well for a variety of parameter combinations. Data on mutational damage in Saccharomyces cereυisiae is used to illustrate the procedures.
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