Generalized Additive Multi-Model for Classification and Prediction
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.
KeywordsLinear Discriminant Analysis Regression Tree Multivariate Adaptive Regression Spline Smoothing Function Semiparametric Model
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- BREIMAN,L.(1996): Bagging predictorsMachine Learning26,46–59.Google Scholar
- BREIMAN, L., FRIEDMAN, J.H., OLSHEN, R.A. and STONE, C.J. (1984):Classification and Regression Trees Belmont C.A. Wadsworth.Google Scholar
- CONVERSANO, C. (1998): A Regression Tree Procedure for Smoothing in Generalized Additive Models. In M. Huskova et al. (eds.):Prague Stochastics’98 Abstracts13–14, Union of Czech Mathematicians and Physicists.Google Scholar
- CONVERSANO, C. (1999)Semiparametric Models for Supervised Classification and Prediction. Some Proposals for Model Integration and Estimation ProceduresPh.D Thesis in Computational Statistics and Data Analysis, Università di Napoli Federico II.Google Scholar
- CONVERSANO, C.,and SICILIANO,R. (1998): A regression tree procedure for smoothing and variable selection in generalized additive models,submitted.Google Scholar
- HASTIE, T.J., and TIBSHIRANI, R.J. (1990): Generalized Additive Models. Chapman and Hall, London.Google Scholar
- MOLA, F. (1998):Selection of cut points in generalized additive models. In M. Vichi and 0. Optiz (eds.): Classification and Data Analysis: Theory and Application, Springer Verlag, Berlin, 121–128.Google Scholar
- SICILIANO,R. and MOLA, F.(1994):Modelling for recursive partitioning and variable selection.In:R.Dutter and R.Grossman (eds.):Compstat’94 Proceedings. Phisyca-Verlag, Heidelberg,172–177.Google Scholar