Abstract
This paper offers, in the framework of generalized additive models (GAM), a proposal of a cut point selection for GAM smoothers that stems out of the CART like regression tree procedures. The proposal allows to find a parsimonious bin smoother (regressogram), a new smoother based on the well known loess smoother, and provides, moreover, the user with an additional information inherited from the regression tree methodology. The problem of the choice of span parameter is considered too.
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© 1999 Springer-Verlag Berlin · Heidelberg
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Mola, F. (1999). Selection of Cut Points in Generalized Additive Models. 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_15
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DOI: https://doi.org/10.1007/978-3-642-60126-2_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65633-3
Online ISBN: 978-3-642-60126-2
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