Generalized functional additive mixed models with (functional) compositional covariates for areal Covid-19 incidence curves
该研究扩展了广义函数型加性混合模型，使其能处理成分和函数型成分协变量，并用于估计西班牙人口年龄、性别和吸烟成分对区域新冠发病率的影响，同时考虑气候、社会人口协变量和空间相关性。
Abstract We extend the generalized functional additive mixed model to include compositional and functional compositional (density) covariates carrying relative information of a whole. Relying on the isometric isomorphism of the Bayes Hilbert space of probability densities with a sub-space of the L2, we include functional compositions as transformed functional covariates with constrained yet interpretable effect function. The extended model allows for the estimation of linear, non-linear, 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.