Factor ModelsEducationRegression AnalysisVector SubspacesPsychologySimultaneous Equation ModelingMultivariate AnalysisFactor AnalysisFactor InteractionStatisticsStructural Equation ModelingBehavioral SciencesLatent Variable ModelFunctional Data AnalysisOrthogonal CaseBusinessEconometricsWay AnalysisInteraction Effect
Interpreting two factor interaction is often difficult, but may sometimes be eased by making use of eovariates associated to each factor. Some linear and non-linear models are considered to this end. In the orthogonal case, the model structure is given by tensor products of vector subspaces. Such a structure naturally applies to main effects. The estimators and their variances'and covariances are given (asymptotic variances in the non-linear case). The choice of a submodel is discussed in the linear case. Finally some remarks are made to generalize these models to models with more than two factors
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