论文标题
部分可观测时空混沌系统的无模型预测
Generalised functional additive mixed models with compositional covariates for areal Covid-19 incidence curves
论文作者
论文摘要
我们将广义功能添加剂混合模型扩展到包含（功能性的）组成协变量，这些协变量包含整体的相对信息。依赖于$ l^2 $的子空间的贝叶斯希尔伯特贝伯特空间的同构同构，我们将功能组合物作为具有约束效应函数的转化功能协变量。扩展模型还允许估计标量和功能协变量的线性，非线性和时变效应，以及（相关）功能随机效应，此外还具有组成效应。我们使用该模型来估计人口的年龄，性别和吸烟（功能）组成对西班牙的区域Covid-19发病率数据的影响，同时考虑了气候和社会人口统计学协变量效应和空间相关性。
We extend the generalised functional additive mixed model to include (functional) compositional covariates carrying relative information of a whole. Relying on the isometric isomorphism of the Bayes Hilbert space of probability densities with a subspace of the $L^2$, we include functional compositions as transformed functional covariates with constrained effect function. The extended model allows for the estimation of linear, nonlinear and time-varying effects of scalar and functional covariates, as well as (correlated) functional random effects, in addition to the compositional effects. We use the model to estimate the effect of the age, sex and smoking (functional) composition of the population on regional Covid-19 incidence data for Spain, while accounting for climatological and socio-demographic covariate effects and spatial correlation.
