Nonparametric estimation of highest density regions for COVID-19
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 behavior 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 analyze a real data set about COVID-19 cases in the United States.
Keywords
Cite
@article{arxiv.2010.14340,
title = {Nonparametric estimation of highest density regions for COVID-19},
author = {Paula Saavedra-Nieves},
journal= {arXiv preprint arXiv:2010.14340},
year = {2020}
}