Robust response transformations for generalized additive models via additivity and variance stabilisation

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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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
Additional Information:	© 2022 Springer.
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	28 Sep 2022 14:12
Last Modified:	11 Mar 2024 16:39
URI:	http://eprints.lse.ac.uk/id/eprint/116688

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