Dr, Department of Mathematic and Statistics, Central China Normal University, Luoyu, Wuhan, 430071, Hubei, China
Research Article
Consistence Condition of Kernel Selection in Regular Linear Kernel Regression and Its Application in COVID-19 High-risk Areas Exploration
Author(s): Lu xan* and Ba lin
With the long-term outbreak of the COVID-19 around the world, identi- fying high-risk areas is becoming a new research boom. In this paper, we propose a novel regression method namely Regular Linear Kernel Regression (RLKR) for COVID-19 high-risk areas exploration. We explain in detail how the canonical linear kernel regression method is linked to the identification of high-risk areas for COVID-19. Furthermore, the consistence condition of Kernel Selection, which is
closely related to the identification of high-risk areas, is given with two mild assumptions. Finally, the RLKR method was verified by simulation experiments and applied for COVID-19 high-risk area Exploration... Read More»
DOI:
10.37421/2736-657X.07.2023.003
Virology: Current Research received 187 citations as per Google Scholar report