Power Comparison of Two Tests of Non-Random Clustering

Abstract

This paper presents two definitions of maximum cluster, that have sometimes been used to test for non-random clustering. We compare the power of the testa based on these statistics, and show that when the clustering interval is sufficiently small, one of the tests is more powerful for a wide class of alternative hypotheses. We show that this test is the generalized likelihood ratio test for an alternative hypothesis related to a particular type of non-random clustering.