Nonparametric estimation of highest density regions for COVID-19

Paula Saavedra-Nieves

Journal of Nonparametric Statistics, 2022, vol. 34, issue 3, 663-682

Abstract: Highest density regions refer to level sets containing points of relatively high density. Their estimation from a random sample, generated from the underlying density, allows to determine the clusters of the corresponding distribution. This task can be accomplished considering different nonparametric perspectives. From a practical point of view, reconstructing highest density regions can be interpreted as a way of determining hot-spots, a crucial task for understanding COVID-19 space-time evolution. In this work, we compare the behaviour of classical plug-in methods and a recently proposed hybrid algorithm for highest density regions estimation through an extensive simulation study. Both methodologies are applied to analyse a real data set about COVID-19 cases in the United States.

Date: 2022
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DOI: 10.1080/10485252.2021.1988083

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