Abstract
Regression modeling often requires many subjective decisions, such as choice of transformation for each variable and the type and number of terms to include in the model. The transformations may be as simple as powers and cross-products or as sophisticated as indicator functions and splines. Sometimes, the transformations are chosen to satisfy certain subjective criteria such as approximate normality of the marginal distributions of the predictor variables. Further, model building is almost always an iterative process, with the fit of the model evaluated each time terms are added or deleted.
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
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. in B. Petrov and F. Csà ki (eds), Second International Symposium on Information Theory, Akademia Kiadó, Budapest, pp. 267–281.
Andrews, D.F. and Herzberg, A.M. (1985). Data: A Collection of Problems from Many Fields for the Student and Research Worker, Springer, New York.
Belsley, D., Kuh, E. and Welsch, R. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, Wiley, New York.
Breiman, L., Friedman, J., Olshen, R. and Stone, C. (1984). Classification and Regression Trees, Wadsworth, Belmont.
Camden, M. (1989). The data bundle, New Zealand Statistical Association, Wellington.
Cook, D. (1998). Regression Graphics: Ideas for Studying Regression Through Graphics, Wiley, New York.
Friedman, L. and Wall, M. (2005). Graphical views of suppression and multicollinearity in multiple linear regression, American Statistician, 59:127–136.
Gilley, O.W. and Pace, R. (1996). On the harrison and rubinfeld data, Journal of Environmental Economics and Management, 31:403–405.
Hallin, M. and Ingenbleek, J.-F. (1983). The swedish automobile portfolio in 1977. A statistical study, Scandinavian Actuarial Journal pp. 49–64.
Harrison, D. and Rubinfeld, D. (1978). Hedonic prices and the demand for clean air, Journal of Environmental Economics and Management, 5:81–102.
Li, K.-C. (1991). Sliced inverse regression for dimension reduction (with discussion), Journal of the American Statistical Association, 86:316–342.
Loh, W.-Y. (2002). Regression trees with unbiased variable selection and interaction detection, Statistica Sinica, 12:361–386.
Miller, A. (2002). Subset Selection in Regression, 2nd edn, Chapman & Hall, London.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Loh, WY. (2008). Regression by Parts: Fitting Visually Interpretable Models with GUIDE. In: Handbook of Data Visualization. Springer Handbooks Comp.Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-33037-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-33037-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33036-3
Online ISBN: 978-3-540-33037-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)