Abstract
In applied regression analysis, model selection criteria are usually used to identify a set of submodels for further study. In this paper, we present a method for a graphical comparison of models that helps in selecting among submodels. The method, based on comparisons of fitted functions projected on two-dimensional surfaces, is offered in a generalized linear models framework, and it is explored in the binomial regression case.
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Research supported by MURST and CNR funds
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References
Bedrick E.J., Tsai C.L. (1994). Model Selection for Multivariate Regression in Small Samples. Biometrics, 50, pp. 226–231.
Bowman A., Young S. (1996). Graphical comparison of nonparametric curves. Applied Statistics, 45, pp. 83–98.
Brown, B.W. (1980). Prediction analysis for binary data, Biostatistics Casebook, Miller, R.J., Efron, B., Brown, B.W. & Moses, L.E. (Eds.), Wiley, New York.
Collett D. (1991). Modelling binary data. Chapman and Hall, 1991.
Cook R.D. (1998). Regression Graphics. Wiley, New York.
Cook R.D., Weisberg, S. (1999). Applied Regression Including Computing and Graphics. Wiley, New York.
Jovanovic, B.D. and Hosmer, D.W. (1997) A simulation of the performance of Cp in model selection for logistic and Poisson regression, Computational Statistics and Data Analysis, 23, pp. 373–379.
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© 2001 Springer-Verlag Berlin Heidelberg
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Porzio, G.C. (2001). A Plot for Submodel Selection in Generalized Linear Models. In: Borra, S., Rocci, R., Vichi, M., Schader, M. (eds) Advances in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59471-7_27
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DOI: https://doi.org/10.1007/978-3-642-59471-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41488-9
Online ISBN: 978-3-642-59471-7
eBook Packages: Springer Book Archive