Copula Dynamic Conditional Correlation and Functional Principal Component Analysis of COVID-19 Mortality in the United States

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Abstract

This paper shows a visual analysis and the dependence relationships of COVID-19 mortality data in 50 states plus Washington, D.C., from January 2020 to 1 September 2022. Since the mortality data are severely skewed and highly dispersed, a traditional linear model is not suitable for the data. As such, we use a Gaussian copula marginal regression (GCMR) model and vine copula-based quantile regression to analyze the COVID-19 mortality data. For a visual analysis of the COVID-19 mortality data, a functional principal component analysis (FPCA), graphical model, and copula dynamic conditional correlation (copula-DCC) are applied. The visual from the graphical model shows five COVID-19 mortality equivalence groups in the US, and the results of the FPCA visualize the COVID-19 daily mortality time trends for 50 states plus Washington, D.C. The GCMR model investigates the COVID-19 daily mortality relationship between four major states and the rest of the states in the US. The copula-DCC models investigate the time-trend dependence relationship between the COVID-19 daily mortality data of four major states.

Original languageEnglish (US)
Article number619
JournalAxioms
Volume11
Issue number11
DOIs
StatePublished - Nov 2022

Bibliographical note

Publisher Copyright:
© 2022 by the author.

Keywords

  • copula-DCC
  • COVID-19
  • functional PCA
  • Gaussian copula regression
  • graphical model
  • mortality
  • vine copula-based quantile regression

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