Consistence Condition of Kernel Selection In Regular Linear Kernel Regression And Its Application In Covid-19 High-risk Areas Exploration

Abstract

With the long-term outbreak of the Covid-19 around the world, identifying 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. Further more, 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.