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Kernel density estimation for circular data about COVID-19 in the Czech Republic
- 1.0544804 - ÚI 2022 FR eng A - Abstrakt
Katina, Stanislav - Zámečník, S. - Hórová, I.
Kernel density estimation for circular data about COVID-19 in the Czech Republic.
ISCB 2021: 42nd Annual Conference of the International Society for Biostatistics: Final Programme & Book of Abstracts. Lyon: ISCB / University Lyon, 2021. s. 244-244.
[ISCB 2021: Annual Conference of the International Society for Biostatistics /42./. 18.07.2021-22.07.2021, Lyon]
Institucionální podpora: RVO:67985807
The term circular statistics describes a set of techniques used to model distributions of random variables that are cyclic in nature and these approaches can be easily adapted to temporal data recorded, e.g., daily, weekly or monthly. One of the nonparametric possibilities how to analyze these data is through kernel estimations of circular densities where the problem of how much to smooth, i.e., how to choose the bandwidth, is crucial. In this presentation we describe the existing methods: cross-validation method, smoothed cross-validation, adaptive method and propose their modifications. We apply these methods on real data from the Institute of health information and statistics of the Czech Republic about total (cumulative) number of persons with a proven COVID-19 infection according to regional hygienic stations, number of cured persons, number of deaths and tests performed for whole country and regions coded based on nomenclature of territorial units for Statistics (NUTS). The results are visualized as circular histograms (rose diagrams) and calculated standardized characteristics are superimposed with choropleth map, where NUTS are shaded in diverging color scheme. All statistical analyses are performed in the R software.
Trvalý link: http://hdl.handle.net/11104/0321611
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