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Our approach based on MFA in the sense of Escofier-Pagès aims at providing a multivariate data analytic technique for applications in biological systems.
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We illustrate the applications of multivariate data analytics techniques to multidimensional functional spectroscopies, which include bias, current, frequency, and time channels in SPM and electron energy loss spectroscopy (EELS) in STEM.
We compared univariate and multivariate data-analytic strategies, but we relied on our multivariate results to elucidate the interaction of attentional and motor networks.
Multivariate data analysis refers to data analytical methods that deal with more than one variable at a time.
Uni- and multivariate data analyses were applied.
PCA projects multivariate data onto a lower dimension orthogonal space.
MCDCOV computes the MCD estimator of a multivariate data set.
This chapter provides models for repeated measures and multivariate data.
Both, spectra and electropherograms can be used as multivariate data.
A possible extension to multivariate data is briefly indicated.
This paper generalizes the Tietjen Moore test for multivariate data to detect the multivariate outliers.
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