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A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy

The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some cov...

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Detalles Bibliográficos
Autores principales: D’Urso, Pierpaolo, De Giovanni, Livia, Vitale, Vincenzina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8744361/
https://www.ncbi.nlm.nih.gov/pubmed/35036295
http://dx.doi.org/10.1016/j.spasta.2021.100586
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author D’Urso, Pierpaolo
De Giovanni, Livia
Vitale, Vincenzina
author_facet D’Urso, Pierpaolo
De Giovanni, Livia
Vitale, Vincenzina
author_sort D’Urso, Pierpaolo
collection PubMed
description The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some covariates of interest accounting for medical, environmental and demographic factors. To deal with the issue of spatial autocorrelation, the D-vine copula based quantile regression also embeds a spatial autoregressive component that controls for the extent of spatial dependence. The use of vine copula enhances model flexibility accounting for non-linear relationships and tail dependencies. Moreover, the model selection procedure leads to parsimonious models providing a rank of covariates based on their explanatory power with respect to the outcome.
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spelling pubmed-87443612022-01-10 A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy D’Urso, Pierpaolo De Giovanni, Livia Vitale, Vincenzina Spat Stat Article The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some covariates of interest accounting for medical, environmental and demographic factors. To deal with the issue of spatial autocorrelation, the D-vine copula based quantile regression also embeds a spatial autoregressive component that controls for the extent of spatial dependence. The use of vine copula enhances model flexibility accounting for non-linear relationships and tail dependencies. Moreover, the model selection procedure leads to parsimonious models providing a rank of covariates based on their explanatory power with respect to the outcome. Elsevier B.V. 2022-03 2022-01-10 /pmc/articles/PMC8744361/ /pubmed/35036295 http://dx.doi.org/10.1016/j.spasta.2021.100586 Text en © 2022 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
D’Urso, Pierpaolo
De Giovanni, Livia
Vitale, Vincenzina
A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy
title A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy
title_full A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy
title_fullStr A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy
title_full_unstemmed A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy
title_short A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy
title_sort d-vine copula-based quantile regression model with spatial dependence for covid-19 infection rate in italy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8744361/
https://www.ncbi.nlm.nih.gov/pubmed/35036295
http://dx.doi.org/10.1016/j.spasta.2021.100586
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