| Abstract: | When employing generalized linear models, interest often focuses on estimation of odds ratios or relative risks. Additionally, researchers often make overall conclusions, requiring accurate estimation of a set of these quantities. Consequently, simultaneous estimation is warranted. Current simultaneous estimation methods only perform well in this setting when there are a very small number of comparisons and/or the sample size is relatively large. Additionally, the estimated quantities can have significant bias especially at small sample sizes. The proposed bounds: (1) perform well for a small or large number of comparisons, (2) exhibit improved performance over current methods for small to moderate sample sizes, (3) provide bias adjustment not reliant on asymptotics, and (4) avoid the infinite parameter estimates that can occur with maximum-likelihood estimators. Simulations demonstrate that the proposed bounds achieve the desired level of confidence at smaller sample sizes than previous methods. |
