Riani, Marco, Atkinson, Anthony C. and Corbellini, Aldo (2022) Robust response transformations for generalized additive models via additivity and variance stabilisation. In: Grilli, L., Lupparelli, M., RampichinI, C., Rocco, E. and Vichi, M., (eds.) Selected papers of 13th Scientific Meeting of Classification and Data Analysis Group - CLADAG 2021. Springer Berlin / Heidelberg, Heidelberg. (In Press)
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Text (Robust Response Transformations for Generalized)
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Abstract
The AVAS (Additivity And Variance Stabilization) algorithm of Tibshirani provides a non-parametric transformation of the response in a linear model to approximately constant variance. It is thus a generalization of the much used Box-Cox transformation. However, AVAS is not robust. Outliers can have a major effect on the estimated transformations both of the response and of the transformed explanatory variables in the Generalized Additive Model (GAM).We describe and illustrate robust methods for the non-parametric transformation of the response and for estimation of the terms in the model and report the results of a simulation study comparing our robust procedure with AVAS. We illustrate the efficacy of our procedure through a simulation study and the analysis of real data.
Item Type: | Book Section |
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Additional Information: | © 2022 Springer. |
Divisions: | Statistics |
Subjects: | H Social Sciences > HA Statistics |
Date Deposited: | 28 Sep 2022 14:12 |
Last Modified: | 16 May 2024 05:58 |
URI: | http://eprints.lse.ac.uk/id/eprint/116688 |
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