ABSTRACT
Goodness-of-Fit Tests Based on Graphical Representation ................... 276
5.2.1. Introduction ................................................................................... 276
5.2.2. The Test Procedure........................................................................ 277
5.2.3. Properties of the Test Statistics..................................................... 279
5.2.4. Extensions of the Test ................................................................... 282
5.2.5. Examples ....................................................................................... 283
5.2.6. Empirical Power ............................................................................ 286
5.2.7. The Test Algorithm ....................................................................... 290
5.2.8. Discussion...................................................................................... 292
Assessing the distributional assumptions about univariate and multivariate data is
a basic concern in statistical applications. A common approach to the problem is to
hypothesize a certain probability model and perform an appropriate goodness-of-
fit test. Various test procedures, some of which are in graphical nature, have been
developed for assessing the distributional assumptions of a sample. For example,
probability plots, in particular, quantile-quantile (Q-Q) plots are among themost
widely used graphical procedures for making assessments about the sample.
