Robust clustering of COVID-19 cases across U.S. counties using mixtures of asymmetric time series models with time varying and freely indexed covariates

Mohsen Maleki, Hamid Bidram and Darren Wraith

Journal of Applied Statistics, 2023, vol. 50, issue 11-12, 2648-2662

Abstract: In this paper, we develop a mixture of autoregressive (MoAR) process model with time varying and freely indexed covariates under the flexible class of two–piece distributions using the scale mixtures of normal (TP-SMN) family. This novel family of time series (TP-SMN-MoAR) models was used to examine flexible and robust clustering of reported cases of Covid-19 across 313 counties in the U.S. The TP-SMN distributions allow for symmetrical/ asymmetrical distributions as well as heavy-tailed distributions providing for flexibility to handle outliers and complex data. Developing a suitable hierarchical representation of the TP-SMN family enabled the construction of a pseudo-likelihood function to derive the maximum pseudo-likelihood estimates via an EM-type algorithm.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2648-2662

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DOI: 10.1080/02664763.2021.2019688

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