A Framework to Interpret Nonstandard Log-Linear Models

Patrick Mair

    Publikation: Wissenschaftliche FachzeitschriftOriginalbeitrag in FachzeitschriftBegutachtung

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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.
    Seiten (von - bis)89-103
    FachzeitschriftAustrian Journal of Statistics
    PublikationsstatusVeröffentlicht - 2007