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The aim of this paper is to model an ordinal response variable in terms ofvector-valued functional data included on a vector-valued RKHS. In particular,we focus on the vector-valued RKHS obtained when a geometrical object (body) ischaracterized by a current and on the ordinal regression model. A common way tosolve this problem in functional data analysis is to express the data in theorthonormal basis given by decomposition of the covariance operator. But ourdata present very important differences with respect to the usual functionaldata setting. On the one hand, they are vector-valued functions, and on theother, they are functions in an RKHS with a previously defined norm. We proposeto use three different bases: the orthonormal basis given by the kernel thatdefines the RKHS, a basis obtained from decomposition of the integral operatordefined using the covariance function, and a third basis that combines theprevious two. The three approaches are compared and applied to an interestingproblem: building a model to predict the fit of children's garment sizes, basedon a 3D database of the Spanish child population.

Generalized Linear Models for Geometrical Current predictors. An application to predict garment fit