Convex Hull Test for Ordered Categorical Data

Abstract

When testing for stochastic order in ordered 2 x J contingency tables, it is common to select the cutoff required to declare significance so as to ensure that the size of the test is exactly alpha conditionally on the margins. It is valid, however, to use the margins to select not only the cutoff but also the form of the test. Linear rank tests, which are locally most powerful and frequently used in practice, suffer from the drawback that they may have power as low as zero to detect some alternatives of interest when the margins satisfy certain conditions. The Smirnov and convex hull tests are shown, through exact conditional power calculations and simulations, to avoid this drawback. The convex hull test is also admissible and palindromic invariant and minimizes the required significance level to have limiting power of one as the alternative moves away from the null in any direction.