Constructing narrower confidence intervals by inverting adaptive tests

SummaryWe begin by describing how to find the limits of confidence intervals by using a few permutation tests of significance. Next, we demonstrate how the adaptive permutation test, which maintains its level of significance, produces confidence intervals that maintain their coverage probabilities. By inverting adaptive tests, adaptive confidence intervals can be found for any single parameter in a multiple regression model. These adaptive confidence intervals are often narrower than the traditional confidence intervals when the error distributions are long ‐tailed or skewed. We show how much reduction in width can be achieved for the slopes in several multiple regression models and for the interaction effect in a two‐way design. AnR function that can compute these adaptive confidence intervals is described and instructions are provided for its use with real data.
Source: Australian and New Zealand Journal of Statistics - Category: Statistics Authors: Tags: Original Article Source Type: research