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Logit-Based Interval Estimation for Binomial Data Using the Jeffreys Prior

Donald B. Rubin and Nathaniel Schenker
Sociological Methodology
Vol. 17 (1987), pp. 131-144
DOI: 10.2307/271031
Stable URL: http://www.jstor.org/stable/271031
Page Count: 14
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Logit-Based Interval Estimation for Binomial Data Using the Jeffreys Prior
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Abstract

Interval estimates for a binomial probability based on normal approximations on the logit scale are evaluated in terms of confidence and Bayesian coverage properties. These estimates, derived as Bayesian posterior intervals under the Jeffreys prior distribution, correspond to large-sample maximum likelihood procedures with a half success and a half failure appended to the data. The intervals work well and are suggestive of prior distributions for more general logistic regression problems.

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