Abstract
In this paper we introduce a methodology based on a combination of classification/prediction procedures derived from different approaches. In particular, starting from a general definition of a classification/prediction model named Generalized Additive Multi-Model (GAM-M) we will demonstrate how it is possible to obtain different types of statistical models based on parametric, semiparametric and nonparametric methods. In our methodology the estimation procedure is based on a variant of the backfitting algorithm used for Generalized Additive Models (GAM). The benchmarking of our methodology will be shown and the results will be compared with those derived from the applications of GAM and Tree procedures.
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Conversano, C., Siciliano, R., Mola, F. (2000). Generalized Additive Multi-Model for Classification and Prediction. In: Kiers, H.A.L., Rasson, JP., Groenen, P.J.F., Schader, M. (eds) Data Analysis, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59789-3_33
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DOI: https://doi.org/10.1007/978-3-642-59789-3_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67521-1
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