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Journal of Applied Mathematics and Decision Sciences - Volume 8 2004, Issue 2, Pages 67-86

School of Quantitative Methods and Mathematical Sciences, University of Western Sydney, Australia

School of Mathematics and Applied Statistics, University of Wollongong, Australia

Copyright © 2004 Hindawi Publishing Corporation. 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.


Log-linear modeling is a popular statistical tool for analysing a contingencytable. This presentation focuses on an alternative approach to modeling ordinal categoricaldata. The technique, based on orthogonal polynomials, provides a much simplermethod of model fitting than the conventional approach of maximum likelihood estimation,as it does not require iterative calculations nor the fitting and re-fitting to searchfor the best model. Another advantage is that quadratic and higher order effects canreadily be included, in contrast to conventional log-linear models which incorporate linearterms only.

The focus of the discussion is the application of the new parameter estimation techniqueto multi-way contingency tables with at least one ordered variable. This will alsobe done by considering singly and doubly ordered two-way contingency tables. It willbe shown by example that the resulting parameter estimates are numerically similar tocorresponding maximum likelihood estimates for ordinal log-linear models.

Autor: Eric J. Beh and Pamela J. Davy

Fuente: https://www.hindawi.com/


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