Characterization Theorems and Goodness-of-Fit Tests

Abstract

Karl Pearson’s chi-square goodness-of-fit test of 1900 is considered an epochal contribution to the science in general and statistics in particular. Regarded as the first objective criterion for agreement between a theory and reality, and suggested as “beginning the prelude to the modern era in statistics,” it stimulated a broadband enquiry into the basics of statistics and led to numerous concepts and ideas which are now common fare in statistical science. Over the decades of the twentieth century the goodness-of-fit has become a substantial field of statistical science of both theoretical and applied importance, and has led to development of a variety of statistical tools. The characterization theorems in probability and statistics, the other topic of our focus, are widely appreciated for their role in clarifying the structure of the families of probability distributions. The purpose of this paper is twofold. The first is to demonstrate that characterization theorems can be natural, logical and effective starting points for constructing goodness-of-fit tests. Towards this end, several entropy and independence characterizations of the normal and the inverse gaussian (IG) distributions, which have resulted in goodness-of-fit tests, are used. The second goal of this paper is to show that the interplay between distributional characterizations and goodness-of-fit assessment continues to be a stimulus for new discoveries and ideas. The point is illustrated using the new concepts of IG symmetry, IG skewness and IG kurtosis, which resulted from goodness-of-fit investigations and have substantially expanded our understanding of the striking and intriguing analogies between the IG and normal families.KeywordsGoodness-of-fitcharacterization