Generalized partially linear models on Riemannian manifolds
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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Title
Generalized partially linear models on Riemannian manifoldsDate
2020-05-03Publisher
Royal Statistical Society; John Wiley & Sons, Inc.Bibliographic citation
SIMÓ, Amelia, et al. Generalized partially linear models on Riemannian manifolds. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2020, 69(3): 641-661Type
info:eu-repo/semantics/articlePublisher version
https://rss.onlinelibrary.wiley.com/doi/10.1111/rssc.12411Version
info:eu-repo/semantics/submittedVersionSubject
Abstract
We introduce generalized partially linear models with covariates on Riemannian manifolds. These models, like ordinary generalized linear models, are a generalization of partially linear models on Riemannian manifolds ... [+]
We introduce generalized partially linear models with covariates on Riemannian manifolds. These models, like ordinary generalized linear models, are a generalization of partially linear models on Riemannian manifolds that allow for scalar response variables with error distribution models other than a normal distribution. Partially linear models are particularly useful when some of the covariates of the model are elements of a Riemannian manifold, because the curvature of these spaces makes it difficult to define parametric models. The model was developed to address an interesting application: the prediction of children's garment fit based on three‐dimensional scanning of their bodies. For this reason, we focus on logistic and ordinal models and on the important and difficult case where the Riemannian manifold is the three‐dimensional case of Kendall's shape space. An experimental study with a well‐known three‐dimensional database is carried out to check the goodness of the procedure. Finally, it is applied to a three‐dimensional database obtained from an anthropometric survey of the Spanish child population. A comparative study with related techniques is carried out. [-]
Investigation project
Spanish Ministry of Economy and Competitiveness with Federación Española de Enfermedades Raras fundsand (grant DPI2017-87333-R) ; Universitat Jaume I (grant UJI-B2017-13)Rights
© 2020 Royal Statistical Society
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