Journal of Applied Mathematics and Decision Sciences
Volume 6 (2002), Issue 4, Pages 241-254
doi:10.1155/S1173912602000172
Components of the Pearson-Fisher chi-squared statistic
National Australia Bank and Fellow of the University of Wollongong, Institute of Mathematical Modelling and Computational Systems, University of Wollongong, Wollongong 2522, NSW, Australia
Copyright © 2002 G. D. Raynery. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The Pearson-Fisher chi-squared test can be used to evaluate the goodness-of-fit of categorized continuous data with known bin endpoints compared to a continuous distribution, in the presence of unknown (nuisance) distribution parameters. Rayner and McAlevey [11] and Rayner and Best [9],[10] demonstrate that in this case, component tests of the Pearson-Fisher chi-squared test statistic can be obtained by equating it to the Neyman smooth score test for a categorized composite null hypothesis under certain restrictions. However, only Rayner and McAlevey [11] provide even brief details as to how these restrictions can be used to obtain any kind of decomposition. More importantly, the relationship between the range of possible decompositions and the
interpretation of the corresponding test statistic components has not previously been investigated. This paper provides the necessary details, as well as an overview of the decomposition options available, and revisits two published examples.