Weighted confidence interval construction for binomial parameters.
Confidence intervals, in general, have become an important aspect of reporting statistical results. In particular, interval estimators for binomial proportions have been studied extensively in recent literature. The large-sample Wald intervals are known to perform poorly, but the Wilson intervals have been shown to perform well in a variety of situations. One criticism is the relative difficulty of computing the Wilson or quadratic intervals in comparison to the Wald intervals. We offer a computational formula for the Wilson intervals that is a weighted estimator of the observed proportion, p, and that based on an uninformative prior, 1/2. This contribution enhances our understanding of the coverage behavior of the Wilson intervals. In addition, we contrast the Wilson intervals with other well-known intervals for the case of zero successes.
Division of Biostatistics, Department of Preventive Medicine, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS 39216, USA. jolivier@prevmed.umsmed.edu
Jake
J
Olivier
Warren L
WL
May
D016001:Confidence Intervals; D015233:Models, Statistical; D014481:United States