Bayesian surface regression versus spatial spectral nonparametric curve regression
Abstract
COVID19 incidence is analyzed at the provinces of some Spanish Communities during the period FebruaryOctober, 2020. Two infinitedimensional regression approaches are tested. The first one is implemented in the regression framework introduced in RuizMedina, Miranda and Espejo (2019). Specifically, a bayesian framework is adopted in the estimation of the pure point spectrum of the temporal autocorrelation operator, characterizing the secondorder structure of a surface sequence. The second approach is formulated in the context of spatial curve regression. A nonparametric estimator of the spectral density operator, based on the spatial periodogram operator, is computed to approximate the spatial correlation between curves. Dimension reduction is achieved by projection onto the empirical eigenvectors of the longrun spatial covariance operator. Crossvalidation procedures are implemented to test the performance of the two functional regression approaches.
 Publication:

arXiv eprints
 Pub Date:
 October 2021
 arXiv:
 arXiv:2111.00302
 Bibcode:
 2021arXiv211100302R
 Keywords:

 Statistics  Methodology;
 Statistics  Applications