COVID-19 incidence is analyzed at the provinces of some Spanish Communities during the period February-October, 2020. Two infinite-dimensional regression approaches are tested. The first one is implemented in the regression framework introduced in Ruiz-Medina, 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 second-order 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 long-run spatial covariance operator. Cross-validation procedures are implemented to test the performance of the two functional regression approaches.

翻译：2020年2月至10月期间,在一些西班牙社区省份对COVID-19的发生率进行了分析,测试了两种无限回归法,第一个是在Ruiz-Medina、Miranda和Espejo(2019年)引入的回归框架中实施的,具体地说,在估计时间自主关系操作员的纯点谱时采用了一个海湾框架,对表面序列的二阶结构进行了定性;第二个方法是在空间曲线回归法背景下制定的;根据空间周期图操作员计算出光谱密度操作员的非参数性估计值,以接近曲线之间的空间相关性;通过预测长期空间共变操作员的经验性负数,使尺寸降低;实施交叉校验程序,以测试两种功能回归法的性能。