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Robust inference for non‐linear regression models from the Tsallis score: Application to coronavirus disease 2019 contagion in Italy
Author(s) -
Girardi Paolo,
Greco Luca,
Mameli Valentina,
Musio Monica,
Racugno Walter,
Ruli Erlis,
Ventura Laura
Publication year - 2020
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.309
Subject(s) - inference , linear regression , regression , covid-19 , econometrics , coronavirus , disease , linear model , statistics , artificial intelligence , computer science , medicine , mathematics , infectious disease (medical specialty)
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.