We discuss an approach of robust fitting on nonlinear regression models, both in a frequentist and a Bayesian approach, which can be employed to model and predict the contagion dynamics of 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 nonlinear regression models.
Robust inference for nonlinear regression models from the Tsallis score: application to COVID-19 contagion in Italy
Mameli, Valentina;Musio, Monica;Racugno, Walter;Ruli, Erlis;Ventura, Laura
2020-01-01
Abstract
We discuss an approach of robust fitting on nonlinear regression models, both in a frequentist and a Bayesian approach, which can be employed to model and predict the contagion dynamics of 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 nonlinear regression models.
File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
sta4.309.pdf
Solo gestori archivio
Tipologia:
versione post-print (AAM)
Dimensione
1.31 MB
Formato
Adobe PDF
|
1.31 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I metadati presenti in IRIS UNICA sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono protetti da diritto d'autore, salvo diversa indicazione.
