COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression

Christian Acal, Manuel Escabias, Ana M. Aguilera and Mariano J. Valderrama
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Christian Acal: Department of Statistics and O.R. and IMAG, University of Granada, 18071 Granada, Spain
Manuel Escabias: Department of Statistics and O.R. and IMAG, University of Granada, 18071 Granada, Spain
Ana M. Aguilera: Department of Statistics and O.R. and IMAG, University of Granada, 18071 Granada, Spain
Mariano J. Valderrama: Department of Statistics and O.R. and IMAG, University of Granada, 18071 Granada, Spain

Mathematics, 2021, vol. 9, issue 11, 1-23

Abstract: 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.

Keywords: functional data analysis; function-on-function regression; functional principal components; B-splines; COVID-19 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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