Abstract
Simultaneous Threshold Interaction Modeling Algorithm (STIMA) has been recently introduced in the framework of statistical modeling as a tool enabling to automatically select interactions in a Generalized Linear Model (GLM) through the estimation of a suitable defined tree structure called ‘trunk’. STIMA integrates GLM with a classification tree algorithm or a regression tree one, depending on the nature of the response variable (nominal or numeric). Accordingly, it can be based on the Classification Trunk Approach (CTA) or on the Regression Trunk Approach (RTA). In both cases, interaction terms are expressed as ‘threshold interactions’ instead of traditional cross-products. Compared with standard tree-based algorithms, STIMA is based on a different splitting criterion as well as on the possibility to ‘force’ the first split of the trunk by manually selecting the first splitting predictor. This paper focuses on model selection in STIMA and it introduces an alternative model selection procedure based on a measure which evaluates the trade-off between goodness of fit and accuracy. Its performance is compared with the one deriving from the current implementation of STIMA by analyzing two real datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Asuncion, A., & Newman, D. J. (2007). UCI machine learning repository, http://archive.ics.uci.edu/ml/.
Berrington de Gonzalez, A., & Cox, D. R. (2007). Interpretation of interaction: A review. Annals of Applied Statistics, 1(2), 371–375.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Belmont, CA: Wadsworth.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.
Conversano, C., & Dusseldorp, E. (2010). Simultaneous threshold interaction detection in binary classification. In C. N. Lauro, M. J. Greenacre, & F. Palumbo (Eds.), Studies in classification, data analysis, and knowledge organization (pp. 225–232). Berlin-Heidelberg: Springer.
Dusseldorp, E., Conversano, C., Van Os, B. J. (2010). Combining an additive and tree-based regression model simultaneously: STIMA. Journal of Computational and Graphical Statistics, forthcoming.
Dusseldorp, E., & Meulman, J. (2004). The regression trunk approach to discover treatment covariate interactions. Psychometrika, 69, 355–374.
Dusseldorp, E., Spinhoven, P., Bakker, A., Van Dyck, R., & Van Balkom, A. J. L. M. (2007). Which panic disorder patients benefit from which treatment: Cognitive therapy or antidepressants? Psychotherapy and Psychosomatics, 76, 154–161.
Friedman, J. H. (1991). Multivariate adaptive regression splines (with discussion). Annals of Statistics, 19, 1–141.
Friedman, J. H., Hastie, T. J., & Tibshirani, R. J. (2001). Elements of statistical learning. New York: Springer.
Hastie, T. J., & Tibshirani, R. J. (1990). Generalized additive models. London, New York: Chapman and Hall.
Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A, 135, 370–384.
McCullagh, P., & Nelder, J. A. (1989). Generalized linear models. London: Chapman & Hall.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Conversano, C. (2011). A Note on Model Selection in STIMA. In: Ingrassia, S., Rocci, R., Vichi, M. (eds) New Perspectives in Statistical Modeling and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11363-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-11363-5_30
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11362-8
Online ISBN: 978-3-642-11363-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)