Robust inference for non-linear regression models from the Tsallis score: Application to coronavirus disease 2019 contagion in Italy

Paolo Girardi; Luca Greco; Valentina Mameli; Monica Musio; Walter Racugno; Erlis Ruli; Laura Ventura

doi:10.1002/sta4.309

Robust inference for non-linear regression models from the Tsallis score: Application to coronavirus disease 2019 contagion in Italy

Stat (Int Stat Inst). 2020;9(1):e309. doi: 10.1002/sta4.309. Epub 2020 Oct 23.

Authors

Paolo Girardi¹, Luca Greco², Valentina Mameli³, Monica Musio⁴, Walter Racugno⁴, Erlis Ruli⁵, Laura Ventura⁵

Affiliations

¹ Department of Developmental Psychology and Socialisation University of Padua Padua 35121 Italy.
² Department of Law, Economics, Management and Quantitative Methods Sannio University Benevento 82100 Italy.
³ Department of Economics and Statistics University of Udine Udine 33100 Italy.
⁴ Department of Mathematics and Informatics University of Cagliari Cagliari 09124 Italy.
⁵ Department of Statistical Sciences University of Padua Padua 35121 Italy.

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.

Keywords: SARS‐CoV‐2 disease; influence function; model misspecification; non‐linear regression; reference prior; scoring rules.