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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INVESTIGACIONMetadata
Title
Generalized functional additive mixed models with (functional) compositional covariates for areal Covid-19 incidence curvesDate
2024-03-19Publisher
Royal Statistical Society; Oxford University PressISSN
0035-9254; 1467-9876Bibliographic citation
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/qlae016Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/publishedVersionSubject
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 ... [+]
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. [-]
Is part of
Journal of the Royal Statistical Society Series C: Applied Statistics, 2024, 00, 1–22 https://doi.org/10.1093/jrsssc/qlae016Funder Name
German Research Association | Ministerio de Ciencia, Innovación y Universidades
Project code
PID2022-141555OB-I00 | GR 3793/8-1
Rights
© The Royal Statistical Society 2024.
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
This item appears in the folowing collection(s)
- MAT_Articles [763]
Except where otherwise noted, this item's license is described as © The Royal Statistical Society 2024.
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