COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression
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AuteurAcal González, Christian José; Escabias Machuca, Manuel; Aguilera Del Pino, Ana María; Valderrama Bonnet, Mariano José
Functional data analysisFunction-on-function regressionFunctional principal componentsB-splinesCOVID-19
Acal, C.; Escabias, M.; Aguilera, A.M.; Valderrama, M.J. COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression. Mathematics 2021, 9, 1237. https:// doi.org/10.3390/math9111237
PatrocinadorMTM2017-88708-P of the Spanish Ministry of Science, Innovation and Universities (also supported by the FEDER program); FQM-307 of the Government of Andalusia (Spain); Ph.D. grant (FPU18/01779)
The aim of this paper is the imputation of missing data of COVID-19 hospitalized and intensive care curves in several Spanish regions. Taking into account that the curves of cases, deceases and recovered people are completely observed, a function-on-function regression model is proposed to estimate the missing values of the functional responses associated with hospitalized and intensive care curves. The estimation of the functional coefficient model in terms of principal components’ regression with the completely observed data provides a prediction equation for the imputation of the unobserved data for the response. An application with data from the first wave of COVID-19 in Spain is developed after properly homogenizing, registering and smoothing the data in a common interval so that the observed curves become comparable. Finally, Canonical Correlation Analysis is performed on the functional principal components to interpret the relationship between hospital occupancy rate and illness response variables.
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