A non-iterative alternative to ordinal Log-Linear models. Issue 2 (2004)

• Record Type: Journal Article
• Authors: Beh, Eric J.; Davy, Pamela J.
• Is Part Of: Journal of applied mathematics & decision sciences. Volume 8:Issue 2(2004)
• Page Start: 67
• Page End: 86
• Publication Date: 2004
• Subjects: Operations research -- Periodicals; Mathematical models -- Periodicals; Statistics -- Periodicals; Quality control -- Periodicals; Mathematical models; Operations research; Quality control; Statistics; Periodicals; 519.05
• DOI: 10.1155/S1173912604000057
• Languages: English
• ISSNs: 1173-9126

Abstract

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 than 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 and higher order effects can readily be included, in contrast to conventional log-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.