Abstract
An alternating method to optimally transform both the response and the regressors in projection pursuit regression is proposed. It is based on alternating the model building stage and the transformation stage. Transformations are deemed optimal with respect to a goodness of fit measure. The main feature of the method is the possibility to deal with mixed data.
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References
Breiman, L. & Friedman, J. H. (1985). Estimating optimal transformations for multiple regression and correlations. Journal of the American Statistical Association, 80, 580–619.
Cuadras, C. M. & Arenas, C. (1990). A distance-based regression model for prediction with mixed data. Communications in Statistics — Theory and Methods, 19, 2261–2279.
Friedman, J. H. (1984a). SMART: User’s Guide, Technical Report no. 1, Department of Statistics and Stanford Linear Accelerator Center, Stanford University.
Friedman, J. H. (1984b). A variable span smoother, Technical Report no. 5, Department of Statistics, Stanford University.
Friedman, J. H. & Stuetzle, W. (1981). Projection pursuit regression. Journal of the American Statistical Association, 76, 817–823.
Gower, J. C. (1966). Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325–338.
Gower, J. C. (1971). A general coefficient of similarity and some of its properties. Biometrics, 27, 857–872.
Gower, J. C. (1987). Contribution to the discussion of Jones, M. C. & Sibson, R. Journal of the Royal Statistical Society, Series A, 150, 19–21.
Laghi, A. & Lizzani, L. (1997). Projection pursuit regression with mixed variables. Book of Short Papers -Classification and Data Analysis, Pescara (Italy), 3–4 July 1997, 181–184.
SAS/IML (1985). User’s guide. Cary, North Carolina: SAS Institute Inc.
Young, F. W. & de Leeuw, J. & Takane, Y. (1976). Regression with qualitative and quantitative variables: an alternating least squares method with optimal scaling features. Psychometrika, 41, 505–529.
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© 1998 Springer-Verlag Berlin Heidelberg
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Laghi, A., Lizzani, L. (1998). An Alternating Method to Optimally Transform Variables in Projection Pursuit Regression. In: Payne, R., Green, P. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-01131-7_50
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DOI: https://doi.org/10.1007/978-3-662-01131-7_50
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1131-5
Online ISBN: 978-3-662-01131-7
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