B1630
Title: COVID-19 incidence analysis from a spatial functional spectral nonparametric approach
Authors: Felicita Doris Miranda Huaynalaya - University of Granada (Spain) [presenting]
Maria Dolores Ruiz-Medina - University of Granada (Spain)
Abstract: Pure point and continuous spectral approaches are adopted for predicting COVID-19 incidence from a Bayesian and a nonparametric framework, respectively. Firstly, we consider a particular example of the dynamical multiple linear regression model in function spaces. The functional regression parameter vector is estimated in terms of the Bayesian approximation of the functional entries of the inverse covariance matrix operator of the Hilbert-valued error term, by applying generalized least-squares estimation. Under this functional linear modeling, spatial correlations are reflected in the matrix covariance operator of the functional error term. Secondly, we adopt a continuous spectral approach, assuming spatial stationarity in the functional correlation model, representing possible interactions between the COVID-19 incidence curves at the Spanish Communities analyzed. We reformulate, for spatially distributed correlated curves, the nonparametric estimator of the spectral density operator, based on the periodogram operator, in the functional time series context. This estimator allows us to compute the functional regression vector parameter estimator to our spatial functional spectral context. To implement the approach proposed, a computation is developed in the real-data analysis of COVID-19 incidence. Particularly, the non-parametric estimator of the spatial-spectral density kernels, at 1061x1061 cross-times, is computed over the 37x37 spatial nodes of the frequency grid.
