Abstract
The aim of this paper is to extend projection pursuit regression to the case of mixed predictors, according to two different approaches. The former consists in converting each categorical regressor into dummy variables. The latter consists in preliminarily transforming the predictors by means of principal coordinate analysis. In presence of strongly non-linear regression functions and interactions between predictors, both procedures improve the results obtained by multiple linear regression, distance-based regression, MORALS and ACE. In particular, projection pursuit regression in conjunction with principal coordinate analysis shows very satisfactory performances.
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© 1999 Springer-Verlag Berlin · Heidelberg
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Laghi, A., Lizzani, L. (1999). Projection Pursuit Regression with Mixed Variables. In: Vichi, M., Opitz, O. (eds) Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60126-2_38
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DOI: https://doi.org/10.1007/978-3-642-60126-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65633-3
Online ISBN: 978-3-642-60126-2
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