Abstract
Generalized linear modelling (GLM) is a versatile statistical technique, which may be viewed as a generalization of well-known techniques such as least squares regression, analysis of variance, loglinear modelling, and logistic regression. In many applications, low-order interaction (such as bivariate interaction) terms are included in the model. However, as the number of categorical variables increases, the total number of low-order interactions also increases dramatically. In this paper, we propose to constrain bivariate interactions by a bi-additive model which allows a simple graphical representation in which each category of every variable is represented by a vector.
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Groenen, P.J., Koning, A.J. (2005). Generalized Bi-Additive Modelling for Categorical Data. In: Bock, HH., et al. New Developments in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27373-5_19
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DOI: https://doi.org/10.1007/3-540-27373-5_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23809-6
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