Summary
This paper discusses alternative strategies in a data analytical situation where one would usually apply multiple regression analysis. The target units of the analysis are a set of predictor variables on the one hand and a criterion variable on the other hand. In the first place we move away from the multiple regression approach by allowing the influence of the predictor set to be channeled through a latent variable that does not have to fit in the predictor space. This technique will be shown to have certain distance properties. These properties are emphazised by introducing a loss function that is defined explicitly on the distances. A special feature of this loss function is that the result of the analysis might be multidimensional. An example is given that investigates the predictive properties.
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© 1988 Springer-Verlag Berlin · Heidelberg
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Meulman, J.J., Heiser, W.J. (1988). Second Order Regression and Distance Analysis. In: Gaul, W., Schader, M. (eds) Data, Expert Knowledge and Decisions. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-73489-2_31
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DOI: https://doi.org/10.1007/978-3-642-73489-2_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-73491-5
Online ISBN: 978-3-642-73489-2
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