Generalized functional additive mixed models with (functional) compositional covariates for areal Covid-19 incidence curves
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comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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INVESTIGACIONMetadatos
Título
Generalized functional additive mixed models with (functional) compositional covariates for areal Covid-19 incidence curvesFecha de publicación
2024-03-19Editor
Royal Statistical Society; Oxford University PressISSN
0035-9254; 1467-9876Cita bibliográfica
Matthias Eckardt, Jorge Mateu, Sonja Greven, Generalized functional additive mixed models with (functional) compositional covariates for areal Covid-19 incidence curves, Journal of the Royal Statistical Society Series C: Applied Statistics, 2024;, qlae016, https://doi.org/10.1093/jrsssc/qlae016Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
Journal of the Royal Statistical Society Series C: Applied Statistics, 2024, 00, 1–22 https://doi.org/10.1093/jrsssc/qlae016Datos relacionados
The R code and data used in the real data applications are made publicly available in a github repository https://github.com/MatkcE/CoDaGFAMM.Entidad financiadora
German Research Association | Ministerio de Ciencia, Innovación y Universidades
Código del proyecto o subvención
PID2022-141555OB-I00 | GR 3793/8-1
Derechos de acceso
© The Royal Statistical Society 2024.
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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Excepto si se señala otra cosa, la licencia del ítem se describe como: © The Royal Statistical Society 2024.
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