We discuss an approach of robust fitting on non-linear regression models, in both frequentist and Bayesian approaches, which can be employed to model and predict the contagion dynamics of the coronavirus disease 2019 (COVID-19) in Italy. The focus is on the analysis of epidemic data using robust dose–response curves, but the functionality is applicable to arbitrary non-linear regression models.

Robust inference for nonlinear regression models from the Tsallis score: application to COVID-19 contagion in Italy

Girardi Paolo;Greco Luca;Mameli Valentina;Ruli Erlis;
2020

Abstract

We discuss an approach of robust fitting on non-linear regression models, in both frequentist and Bayesian approaches, which can be employed to model and predict the contagion dynamics of the coronavirus disease 2019 (COVID-19) in Italy. The focus is on the analysis of epidemic data using robust dose–response curves, but the functionality is applicable to arbitrary non-linear regression models.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3757396
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