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

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. Studies in Classification, Data Analysis, and Knowledge Organization. Springer Nature Switzerland, Cham, Switzerland, 147 - 159. ISBN 9783031301636

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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
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:	28 Sep 2022 14:12
Last Modified:	19 Nov 2024 21:15
URI:	http://eprints.lse.ac.uk/id/eprint/116688

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