Abstract
This paper reviews some models for exploring multivariate data. If a fixed effect model is used to define a linear Principal Components Analysis (PCA), then risk functions can be defined and issues of metric and dimension optimality addressed. The model is then adapted to define a functional PCA which can be used to the study of smooth sampled curves. Finally, this model is generalised, giving a curvilinear PCA, which attempts to build smooth optimal transformations of data for the purpose of dimension reduction.
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© 1994 Springer-Verlag Berlin Heidelberg
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Besse, P.C. (1994). Models for Multivariate Data Analysis. In: Dutter, R., Grossmann, W. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-52463-9_31
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DOI: https://doi.org/10.1007/978-3-642-52463-9_31
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0793-6
Online ISBN: 978-3-642-52463-9
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