Models for Multivariate Data Analysis
نویسنده
چکیده
This paper reviews some models for exploring multivariate data. If a xed eeect model is used to deene a linear Principal Components Analysis (PCA), then risk functions can be deened and issues of metric and dimension optimality addressed. The model is then adapted to deene 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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تاریخ انتشار 2007