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- Title
A Non-Iterative Alternative to Ordinal Log-Linear Models.
- Authors
Beh, Eric J.; Davy, Pamela J.
- Abstract
Log-linear modeling is a popular statistical tool for analysing a contingency table. This presentation focuses on an alternative approach to modeling ordinal categorical data. The technique, based on orthogonal polynomials, provides a much simpler method of model fitting that a the conventional approach of maximum likelihood estimation, as it does not require iterative calculations nor the fitting and re-fitting to search for the best model, Another advantage is that quadratic arid higher order effects can readily be included, in contrast to conventional tog-linear models which incorporate linear terms only. The focus of the discussion is the application of the new parameter estimation technique to multi-way contingency tables with at least one ordered variable. This will also be done by considering singly and doubly ordered two-way contingency tables. It will be shown by example that the resulting parameter estimates are numerically similar to corresponding maximum likelihood estimates for ordinal log-linear models.
- Subjects
LINEAR statistical models; POLYNOMIALS; MATHEMATICAL models; ESTIMATION theory; MATHEMATICAL statistics; MATHEMATICS
- Publication
Journal of Applied Mathematics & Decision Sciences, 2004, Vol 8, Issue 2, p67
- ISSN
1173-9126
- Publication type
Article
- DOI
10.1155/S1173912604000057