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• W3206762937 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." @default.
• W3206762937 created "2021-10-25" @default.
• W3206762937 creator A5029925293 @default.
• W3206762937 date "2021-10-18" @default.
• W3206762937 modified "2023-10-06" @default.
• W3206762937 title "Nonparametric estimation of highest density regions for COVID-19" @default.
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• W3206762937 doi "https://doi.org/10.1080/10485252.2021.1988083" @default.
• W3206762937 hasPublicationYear "2021" @default.
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