Abstract
Starting from the results obtained by recursive partitioning methods, radial basis function networks for longitudinal data are derived. The aim of this work is to show how the joint use of the two methods allows to overcome some drawbacks that they show when they are used separately. More precisely, this strategy allows not only to obtain a smooth nonparametric estimate of the regression surface, but also to automatically determine the model complexity and to perform a covariate selection. The performances of the proposed strategy are evaluated on simulated data sets.
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Pillati, M., Calò, D.G., Galimberti, G. (2003). Combining Regression Trees and Radial Basis Function Networks in Longitudinal Data Modelling. In: Schader, M., Gaul, W., Vichi, M. (eds) Between Data Science and Applied Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18991-3_10
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DOI: https://doi.org/10.1007/978-3-642-18991-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40354-8
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