Statistical Distribution Models: Goodness of Fit Tests

Authors

J. C. W Rayner, University of WollongongFollow
Olivier Thas, Ghent UniversityFollow
Donald J. Best, University of NewcastleFollow

RIS ID

123732

Publication Details

Rayner, J. C. W., Thas, O. & Best, D. J. (2015). Statistical Distribution Models: Goodness of Fit Tests. In J. D. Wright (Ed.), International Encyclopedia of the Social & Behavioral Sciences (pp. 397-404). United States: Elsevier.

Abstract

The purpose of a one-sample test of fit is to give an objective measure of how well a probability model agrees with observed data. Here we discuss the test of Karl Pearson and derivatives of it, tests based on the empirical distribution function and the construction of the Neyman-Barton smooth tests. In the final section, we then address some modern developments in smooth testing: diagnostics, Cholesky components, data-driven tests and model selection. Other tests of fit, such as correlation tests and Laplace transform tests, are not considered here.