RMSE-minimizing confidence intervals for the binomial parameter

Kexin Feng, Lawrence M. Leemis () and Heather Sasinowska
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Kexin Feng: William & Mary
Lawrence M. Leemis: William & Mary
Heather Sasinowska: William & Mary

Computational Statistics, 2022, vol. 37, issue 4, No 12, 1855-1885

Abstract: Abstract Let X be the number of successes in n mutually independent and identically distributed Bernoulli trials, each with probability of success p. For fixed n and $$\alpha $$ α , there are $$n + 1$$ n + 1 distinct two-sided $$100(1 - \alpha )$$ 100 ( 1 - α ) % confidence intervals for p associated with the outcomes $${X = 0, 1, 2, \ldots , n}$$ X = 0 , 1 , 2 , … , n . There is no known exact non-randomized confidence interval for p. Existing approximate confidence interval procedures use a formula, which often requires numerical methods to implement, to calculate confidence interval bounds. The bounds associated with these confidence intervals correspond to discontinuities in the actual coverage function. The paper does not aim to provide a formula for the confidence interval bounds, but rather to select the confidence interval bounds that minimize the root mean square error of the actual coverage function for sample size n and significance level $$\alpha $$ α in the frequentist context.

Keywords: Actual coverage function; Approximate confidence interval; Binary data; Binomial distribution; Dyck word (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01183-3

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