Riani, Marco, Atkinson, Anthony C. and Corbellini, Aldo (2023) Robust response transformations for generalized additive models via additivity and variance stabilization. In: Grilli, Leonardo, Lupparelli, Monia, Rampichini, Carla, Rocco, Emilia and Vichi, Maurizio, (eds.) Statistical Models and Methods for Data Science. Statistical Models and Methods for Data Science. Springer Nature Switzerland, Cham, Switzerland, 147 - 159. ISBN 9783031301636
Full text not available from this repository.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 |
|---|---|
| Official URL: | https://doi.org/10.1007/978-3-031-30164-3 |
| Additional Information: | © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG |
| Divisions: | Statistics |
| Subjects: | Q Science > QA Mathematics |
| Date Deposited: | 24 May 2024 14:09 |
| Last Modified: | 06 Jul 2024 01:54 |
| URI: | http://eprints.lse.ac.uk/id/eprint/123633 |
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