A simulation study of a proposed graphical diagnostic for assessing goodness-of-fit

The question addressed is the choice of a final model from suitable models being evaluated. Kaiser et al. (J. Amer. Statist. Assoc. 89(426) (1994) 410) proposed a graphical goodness-of-fit diagnostic to address the question of whether a model provides an acceptable description of the data. The method is based on the probability integral transformation and on measures such as the Kolmogorov-Smimov one-sample goodness-of-fit (GOF) test. Working under the generalized linear models framework, we simulate data from lognormal and gamma distributions with the same mean and variance. For each distribution, a correct and a misspecified model in terms of random component and link function are fitted. We evaluate the proposed graphical diagnostic tool as a guide for model selection, specifically when used along with the Kolmogorov-Smimov one-sample GOF test measure as a guide to decision making. (C) 2002 Elsevier Science B.V. All rights reserved.