ORCID Profile
0000-0002-0998-6174
Current Organisation
University of Oxford
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Publisher: Informa UK Limited
Date: 22-05-2012
Publisher: Public Library of Science (PLoS)
Date: 15-07-2015
Publisher: Institute of Mathematical Statistics
Date: 09-2012
DOI: 10.1214/12-BA717
Publisher: Cold Spring Harbor Laboratory
Date: 03-11-2020
DOI: 10.1101/2020.10.28.20221077
Abstract: Hawkes processes are a form of self-exciting process that has been used in numerous applications, including neuroscience, seismology, and terrorism. While these self-exciting processes have a simple formulation, they are able to model incredibly complex phenomena. Traditionally Hawkes processes are a continuous-time process, however we enable these models to be applied to a wider range of problems by considering a discrete-time variant of Hawkes processes. We illustrate this through the novel coronavirus disease (COVID-19) as a substantive case study. While alternative models, such as compartmental and growth curve models, have been widely applied to the COVID-19 epidemic, the use of discrete-time Hawkes processes allows us to gain alternative insights. This paper evaluates the capability of discrete-time Hawkes processes by retrospectively modelling daily counts of deaths as two distinct phases in the progression of the COVID-19 outbreak: the initial stage of exponential growth and the subsequent decline as preventative measures become effective. We consider various countries that have been adversely affected by the epidemic, namely, Brazil, China, France, Germany, India, Italy, Spain, Sweden, the United Kingdom and the United States. These countries are all unique concerning the spread of the virus and their corresponding response measures, in particular, the types and timings of preventative actions. However, we find that this simple model is useful in accurately capturing the dynamics of the process, despite hidden interactions that are not directly modelled due to their complexity, and differences both within and between countries. The utility of this model is not confined to the current COVID-19 epidemic, rather this model could be used to explain many other complex phenomena. It is of interest to have simple models that adequately describe these complex processes with unknown dynamics. As models become more complex, a simpler representation of the process can be desirable for the sake of parsimony.
Publisher: Oxford University Press (OUP)
Date: 09-08-2011
DOI: 10.1111/J.1467-9868.2011.00781.X
Abstract: We study the asymptotic behaviour of the posterior distribution in a mixture model when the number of components in the mixture is larger than the true number of components: a situation which is commonly referred to as an overfitted mixture. We prove in particular that quite generally the posterior distribution has a stable and interesting behaviour, since it tends to empty the extra components. This stability is achieved under some restriction on the prior, which can be used as a guideline for choosing the prior. Some simulations are presented to illustrate this behaviour.
Publisher: Institute of Mathematical Statistics
Date: 09-2016
DOI: 10.1214/16-AOAS944
Publisher: Institute of Mathematical Statistics
Date: 2010
DOI: 10.1214/09-EJS527
Publisher: Wiley
Date: 27-04-2008
Publisher: Institute of Mathematical Statistics
Date: 03-2014
DOI: 10.1214/13-AOAS678
Location: United Kingdom of Great Britain and Northern Ireland
Location: France
No related grants have been discovered for Judith Rousseau.