A Framework to Interpret Nonstandard Log-Linear Models

Mair, Patrick (2007) A Framework to Interpret Nonstandard Log-Linear Models. Austrian Journal of Statistics, 36 (2). pp. 89-103. ISSN 1026-597X

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The formulation of log-linear models within the framework of Generalized Linear Models offers new possibilities in modeling categorical data. The resulting models are not restricted to the analysis of contingency tables in terms of ordinary hierarchical interactions. Such models are considered as the family of nonstandard log-linear models. The problem that can arise is an ambiguous interpretation of parameters. In the current paper this problem is solved by looking at the effects coded in the design matrix and determining the numerical contribution of single effects. Based on these results, stepwise approaches are proposed in order to achieve parsimonious models. In addition, some testing strategies are presented to test such (eventually non-nested) models against each other. As a result, a whole interpretation framework is elaborated to examine nonstandard log-linear models in depth.

Item Type: Article
Keywords: Dominance Analysis, Stepwise Selection, Non-Nested Model Testing.
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Version of the Document: Published
Depositing User: ePub Administrator
Date Deposited: 11 Jul 2017 10:34
Last Modified: 10 Nov 2017 06:49
Related URLs:
URI: https://epub.wu.ac.at/id/eprint/5636


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