A kernel regression procedure in the 3D shape space with an application to online sales of children's wear
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Title
A kernel regression procedure in the 3D shape space with an application to online sales of children's wearDate
2019Publisher
Institute of Mathematical Statistics (IMS)ISSN
0883-4237; 2168-8745Bibliographic citation
Quintana-Ortí, Gregorio; Simó Amelia. A Kernel Regression Procedure in the 3D Shape Space with an Application to Online Sales of Children’s Wear. Statistical Science, 2019, vol. 34, núm. 2, p. 236-252Type
info:eu-repo/semantics/articlePublisher version
https://projecteuclid.org/euclid.ss/1563501640#abstractVersion
info:eu-repo/semantics/submittedVersionSubject
Abstract
Shape regression is of key importance in many scienti c elds. In this paper,
we focus on the case where the shape of an object is represented by a con-
guration matrix of landmarks. It is well known that this shape ... [+]
Shape regression is of key importance in many scienti c elds. In this paper,
we focus on the case where the shape of an object is represented by a con-
guration matrix of landmarks. It is well known that this shape space has
a nite-dimensional Riemannian manifold structure (non-Euclidean) which
makes it di cult to work with. Papers about regression on this space are
scarce in the literature. The majority of them are restricted to the case of a
single explanatory variable, usually time or age, and many of them work in
the approximated tangent space. In this paper we adapt the general method
for kernel regression analysis in manifold-valued data proposed by Davis et al
(2007) to the three-dimensional case of Kendall's shape space and generalize
it to multiple explanatory variables. We also propose bootstrap con dence
intervals for prediction. A simulation study is carried out to check the goodness
of the procedure, and nally it is applied to a 3D database obtained from
an anthropometric survey of the Spanish child population with a potential
application to online sales of children's wear. [-]
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Statistical Science, 2019, vol. 34, núm. 2, p. 236-252Rights
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info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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